update references to preview.1

.github/pull_request_template.md

 # Peer Review Information:
 
 ## Editor / Package versioning:
-**Editor Version Target**: 2019.4
+**Editor Version Target**: 2020.3
 
 ## Dev Testing:
 **Tests Added**: 
 **At Risk Areas**: 
 
 ## Checklist
-- [ ] - Updated docs
-- [ ] - Updated changelog
-- [ ] - Updated test rail
+- [ ] Updated docs
+- [ ] Updated changelog
+- [ ] Updated test rail

.yamato/environments.yml

 
 upmci_registry: https://artifactory.prd.cds.internal.unity3d.com/artifactory/api/npm/upm-npm
 
-# sticking to 2019.4.6f1 for testing for now because Linux Editor fails to open PerceptionHDRP on 2019.4.8f1
-# see https://fogbugz.unity3d.com/default.asp?1273518_d68j5lb6eucglb84
-#   - version: 2019.4.18f1
-  - version: 2020.3.17f1
+  - version: 2020.3.19f1
+  - version: 2021.1.23f1
-#   - version: 2019.4.18f1
-  - version: 2020.3.17f1
+  - version: 2020.3.19f1
+  - version: 2021.1.23f1
-#   - version: 2019.4.18f1
-  - version: 2020.3.17f1
+  - version: 2020.3.19f1
+  - version: 2021.1.23f1
-#  - version: 2019.4.18f1
-  - version: 2020.3.17f1
+  - version: 2020.3.19f1
+  - version: 2021.1.23f1
 
 publish_platforms:
   - name: win

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+  simulationDeltaTime: 0.0166
+  framesBetweenCaptures: 0
   showVisualizations: 1
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-  m_LocalScale: {x: 36.249973, y: 36.249973, z: 36.249973}
+  m_LocalPosition: {x: 65.9, y: 83.926025, z: -212.4}
+  m_LocalScale: {x: 36.249973, y: 100, z: 36.249973}
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   m_Name: UniversalRPAsset
   m_EditorClassIdentifier: 
-  k_AssetVersion: 5
+  k_AssetVersion: 6
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   m_RendererData: {fileID: 0}
   m_OpaqueDownsampling: 1
   m_SupportsTerrainHoles: 1
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+  m_MSAA: 1
   m_RenderScale: 1
   m_MainLightRenderingMode: 1
   m_MainLightShadowsSupported: 1
   m_AdditionalLightShadowsSupported: 1
   m_AdditionalLightsShadowmapResolution: 512
   m_ShadowDistance: 50
-  m_ShadowCascades: 1
+  m_ShadowCascadeCount: 2
+  m_Cascade3Split: {x: 0.1, y: 0.3}
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   m_ShadowDepthBias: 1
   m_ShadowNormalBias: 1
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-  m_PostProcessingFeatureSet: 0
+  m_UseAdaptivePerformance: 1
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   m_ShadowType: 1
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   m_ShaderVariantLogLevel: 0
+  m_VolumeFrameworkUpdateMode: 0
+  m_ShadowCascades: 1

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README.md

 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE.md)
 
-<img src="https://img.shields.io/badge/unity-2019.4-green.svg?style=flat-square" alt="unity 2019.4">
-<img src="https://img.shields.io/badge/unity-2020.2-green.svg?style=flat-square" alt="unity 2020.3">
+<img src="https://img.shields.io/badge/unity-2020.3-green.svg?style=flat-square" alt="unity 2020.3">
+<img src="https://img.shields.io/badge/unity-2021.2-green.svg?style=flat-square" alt="unity 2021.2">
 
 > `com.unity.perception` is in active development. Its features and API are subject to significant change as development progresses.
 
 
 **[FAQ](com.unity.perception/Documentation~/FAQ/FAQ.md)**  
 Check out our FAQ for a list of common questions, tips, tricks, and some sample code.
+
+**[Verifying Datasets with Dataset Insights](com.unity.perception/Documentation~/Tutorial/DatasetInsights.md)**  
+Introduction to Unity's [Dataset Insights](https://github.com/Unity-Technologies/datasetinsights) – a python package for downloading, parsing and analyzing synthetic datasets.
 
 ## Documentation
 In-depth documentation on individual components of the package. 
 * To allow navigating to code in all packages included in your project, in your Unity Editor, navigate to `Edit -> Preferences... -> External Tools` and check `Generate all .csproj files.` 
 
 ## Known issues
-
-* The Linux Editor 2019.4.7f1 and 2019.4.8f1 might hang when importing HDRP-based Perception projects. For Linux Editor support, use 2019.4.6f1 or 2020.1
+* The Linux Editor 2019.4.7f1 and 2019.4.8f1 might hang when importing HDRP-based Perception projects. For Linux Editor support, use 2020.3 or 2021.2.
+* Projects that use the Perception package on Windows or OS X will have a dependency for Python for Unity added to their manifest, in order for the new Dataset Visualizer tool to work. This tool and Python for Unity are not supported on Linux, therefore this dependency should be removed from the project's manifest file if the project is saved on Windows or OSX and opened on Linux.
 
 ## License
 * [License](com.unity.perception/LICENSE.md)

com.unity.perception/Tests/Runtime/Unity.Perception.Runtime.Tests.asmdef

         "Unity.Perception.Runtime",
         "Unity.Perception.Editor",
         "Unity.Simulation.Core",
+        "Unity.Simulation.Capture",
         "Unity.RenderPipelines.HighDefinition.Runtime",
         "Unity.RenderPipelines.Universal.Runtime"
     ],
         }
     ],
     "noEngineReferences": false
-}
+}

com.unity.perception/Tests/Runtime/GroundTruthTests/SegmentationGroundTruthTests.cs

             Assert.AreEqual(1, timesSegmentationImageReceived);
         }
 
+
+        [UnityTest]
+        public IEnumerator SegmentationPass_WithMultiplePerceptionCameras_ProducesCorrectValues(
+            [Values(SegmentationKind.Instance, SegmentationKind.Semantic)] SegmentationKind segmentationKind)
+        {
+            int timesSegmentationImageReceived = 0;
+
+            var color1 = segmentationKind == SegmentationKind.Instance ?
+                k_InstanceSegmentationPixelValue :
+                k_SemanticPixelValue;
+            var color2 = segmentationKind == SegmentationKind.Instance ?
+                new Color32(0, 74, Byte.MaxValue, Byte.MaxValue) :
+                new Color32(0, 0, 0, Byte.MaxValue);
+            void OnCam1SegmentationImageReceived(NativeArray<Color32> data)
+            {
+                CollectionAssert.AreEqual(Enumerable.Repeat(color1, data.Length), data);
+                timesSegmentationImageReceived++;
+            }
+            void OnCam2SegmentationImageReceived(NativeArray<Color32> data)
+            {
+                Assert.AreEqual(color1, data[data.Length / 4]);
+                Assert.AreEqual(color2, data[data.Length * 3 / 4]);
+                timesSegmentationImageReceived++;
+            }
+
+            GameObject cameraObject;
+            GameObject cameraObject2;
+            if (segmentationKind == SegmentationKind.Instance)
+            {
+                cameraObject = SetupCameraInstanceSegmentation((frame, data, renderTexture) => OnCam1SegmentationImageReceived(data));
+                cameraObject2 = SetupCameraInstanceSegmentation((frame, data, renderTexture) => OnCam2SegmentationImageReceived(data));
+            }
+            else
+            {
+                cameraObject = SetupCameraSemanticSegmentation((args) => OnCam1SegmentationImageReceived(args.data), false);
+                cameraObject2 = SetupCameraSemanticSegmentation((args) => OnCam2SegmentationImageReceived(args.data), false);
+            }
+            //position camera to point straight at the top edge of plane1, such that plane1 takes up the bottom half of
+            //the image and plane2 takes up the top half
+            cameraObject2.transform.localPosition = Vector3.up * 2.5f;
+
+            var plane1 = TestHelper.CreateLabeledPlane(.5f);
+            var plane2 = TestHelper.CreateLabeledPlane(.5f, "label2");
+            plane2.transform.localPosition = plane2.transform.localPosition + Vector3.up * 5f;
+            AddTestObjectForCleanup(plane1);
+            AddTestObjectForCleanup(plane2);
+            yield return null;
+
+            //destroy the object to force all pending segmented image readbacks to finish and events to be fired.
+            DestroyTestObject(cameraObject);
+            DestroyTestObject(cameraObject2);
+            Assert.AreEqual(2, timesSegmentationImageReceived);
+        }
+
         [UnityTest]
         public IEnumerator SegmentationPassProducesCorrectValuesEachFrame(
             [Values(SegmentationKind.Instance, SegmentationKind.Semantic)] SegmentationKind segmentationKind)

com.unity.perception/Tests/Runtime/Randomization/RandomizerTests/RandomizerTests.cs

 using System.Collections;
 using NUnit.Framework;
+using RandomizationTests.ScenarioTests;
+using UnityEngine.Perception.GroundTruth;
+using UnityEngine.Perception.Randomization.Randomizers;
 using UnityEngine.Perception.Randomization.Scenarios;
 using UnityEngine.TestTools;
 
         }
 
         // TODO: update this function once the perception camera doesn't skip the first frame
-        IEnumerator CreateNewScenario(int totalIterations, int framesPerIteration)
+        IEnumerator CreateNewScenario(int totalIterations, int framesPerIteration, Randomizer[] randomizers = null)
-            yield return null; // Skip first frame
+
+            if (randomizers != null)
+            {
+                foreach (var rnd in randomizers)
+                {
+                    m_Scenario.AddRandomizer(rnd);
+                }
+            }
+
+            if (PerceptionCamera.captureFrameCount < 0)
+            {
+                yield return null;
+            }
         }
 
         [Test]
         [UnityTest]
         public IEnumerator OnUpdateExecutesEveryFrame()
         {
-            yield return CreateNewScenario(10, 1);
-            m_Scenario.AddRandomizer(new ExampleTransformRandomizer());
+            yield return CreateNewScenario(10, 1, new Randomizer[] { new ExampleTransformRandomizer() });
             var transform = m_TestObject.transform;
             var initialPosition = Vector3.zero;
             transform.position = initialPosition;
         [UnityTest]
         public IEnumerator OnIterationStartExecutesEveryIteration()
         {
-            yield return CreateNewScenario(10, 2);
-            m_Scenario.AddRandomizer(new ExampleTransformRandomizer());
+            yield return CreateNewScenario(10, 2, new Randomizer[] { new ExampleTransformRandomizer() });
+
+            // Wait one frame so the next iteration can begin
+            yield return null;
 
             yield return null;
             Assert.AreNotEqual(initialRotation, transform.rotation);

com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/ScenarioTests.cs

 using System;
 using System.Collections;
 using System.Collections.Generic;
-using System.Linq;
+using Newtonsoft.Json;
+using Unity.Collections;
+using UnityEngine.Perception.Analytics;
+using UnityEngine.Perception.RandomizationTests.ScenarioTests;
 using UnityEngine.TestTools;
 using Object = UnityEngine.Object;
 
     {
         GameObject m_TestObject;
         TestFixedLengthScenario m_Scenario;
+
+        static string RemoveWhitespace(string str) =>
+            string.Join("", str.Split(default(string[]), StringSplitOptions.RemoveEmptyEntries));
 
         [SetUp]
         public void Setup()
                 }
             }
 
-            yield return null; // Skip first frame
-            yield return null; // Skip first Update() frame
+            if (PerceptionCamera.captureFrameCount < 0)
+            {
+                yield return null;
+            }
         }
 
         [Test]
             m_Scenario = m_TestObject.AddComponent<TestFixedLengthScenario>();
             m_Scenario.AddRandomizer(new RotationRandomizer());
 
-            string RemoveWhitespace(string str) =>
-                string.Join("", str.Split(default(string[]), StringSplitOptions.RemoveEmptyEntries));
-
             var expectedConfigAsset = (TextAsset)Resources.Load("SampleScenarioConfiguration");
             var expectedText = RemoveWhitespace(expectedConfigAsset.text);
             var scenarioJson = RemoveWhitespace(m_Scenario.SerializeToJson());
                 Assert.AreEqual(0, m_Scenario.currentIteration);
                 yield return null;
             }
+
             Assert.AreEqual(1, m_Scenario.currentIteration);
         }
 
                 Assert.True(m_Scenario.state == ScenarioBase.State.Playing);
                 yield return null;
             }
+
             Assert.True(m_Scenario.state == ScenarioBase.State.Idle);
         }
 
             for (var i = 0; i < 3; i++)
                 Assert.AreNotEqual(seeds[i], SamplerState.NextRandomState());
         }
-
 
         [UnityTest]
         public IEnumerator IterationCorrectlyDelays()
             // State: currentIteration = 0
             Assert.AreEqual(0, m_Scenario.currentIteration);
             yield return null;
+
+
+
+
+
+
+
+        }
+
+        [UnityTest]
+        public IEnumerator ScenarioCompletedAnalyticsSerializesCorrectly()
+        {
+            // Perception Camera Serialization
+            var perceptionCamera = m_TestObject.AddComponent<PerceptionCamera>();
+            perceptionCamera.captureTriggerMode = CaptureTriggerMode.Scheduled;
+            perceptionCamera.firstCaptureFrame = 2;
+            perceptionCamera.framesBetweenCaptures = 10;
+
+            // Labeler serialization
+            var sampleIdLabelConfig = Resources.Load<IdLabelConfig>("sampleIdLabelConfig");
+            var sampleAnimationPoseConfig = Resources.Load<AnimationPoseConfig>("sampleAnimationPoseConfig");
+            var emptyKeypointTemplate = ScriptableObject.CreateInstance<KeypointTemplate>();
+            emptyKeypointTemplate.keypoints = new KeypointDefinition[] { };
+            emptyKeypointTemplate.skeleton = new SkeletonDefinition[] { };
+
+            perceptionCamera.AddLabeler(new BoundingBox2DLabeler(sampleIdLabelConfig));
+            perceptionCamera.AddLabeler(new RenderedObjectInfoLabeler(sampleIdLabelConfig));
+            perceptionCamera.AddLabeler(new KeypointLabeler()
+            {
+                idLabelConfig = sampleIdLabelConfig,
+                objectFilter = KeypointObjectFilter.Visible,
+                activeTemplate = emptyKeypointTemplate,
+                animationPoseConfigs = new List<AnimationPoseConfig>()
+                {
+                    sampleAnimationPoseConfig, sampleAnimationPoseConfig
+                }
+            });
+
+            // Randomizer Serialization
+            var randomizers = new List<Randomizer>();
+            var testRandomizer = new AllMembersAndParametersTestRandomizer();
+            testRandomizer.colorRgbCategoricalParam.SetOptions(new (Color, float)[]
+            {
+                (Color.black, 0.4f),
+                (Color.blue, 0.93f),
+                (Color.red, 0.23f)
+            });
+            randomizers.Add(testRandomizer);
+
+            yield return null;
+
+            // Scenario Completed Serialization
+            var scenarioCompletedData = ScenarioCompletedData.FromCameraAndRandomizers(perceptionCamera, randomizers);
+
+            #region expectedRandomizerJson
+            var expectedRandomizerJson = RemoveWhitespace($@"
+{{
+  ""platform"": ""{Application.platform}"",
+  ""perceptionCamera"": {{
+    ""captureTriggerMode"": ""Scheduled"",
+    ""startAtFrame"": 2,
+    ""framesBetweenCaptures"": 10
+  }},
+  ""labelers"": [
+    {{ ""name"":  ""BoundingBox2DLabeler"", ""labelConfigCount"": 3, ""objectFilter"": """", ""animationPoseCount"":  0 }},
+    {{ ""name"":  ""RenderedObjectInfoLabeler"", ""labelConfigCount"": 3, ""objectFilter"": """", ""animationPoseCount"":  0  }},
+    {{ ""name"":  ""KeypointLabeler"", ""labelConfigCount"": 3, ""objectFilter"": ""Visible"", ""animationPoseCount"": 2 }}
+  ],
+  ""randomizers"": [
+    {{
+      ""name"": ""AllMembersAndParametersTestRandomizer"",
+      ""members"": [
+        {{
+          ""name"": ""booleanMember"",
+          ""value"": ""False"",
+          ""type"": ""System.Boolean""
+        }},
+        {{
+          ""name"": ""intMember"",
+          ""value"": ""4"",
+          ""type"": ""System.Int32""
+        }},
+        {{
+          ""name"": ""uintMember"",
+          ""value"": ""2"",
+          ""type"": ""System.UInt32""
+        }},
+        {{
+          ""name"": ""floatMember"",
+          ""value"": ""5"",
+          ""type"": ""System.Single""
+        }},
+        {{
+          ""name"": ""vector2Member"",
+          ""value"": ""(4.0, 7.0)"",
+          ""type"": ""UnityEngine.Vector2""
+        }},
+        {{
+          ""name"": ""unsupportedMember"",
+          ""value"": ""UnityEngine.Perception.Randomization.Samplers.UniformSampler"",
+          ""type"": ""UnityEngine.Perception.Randomization.Samplers.UniformSampler""
+        }}
+      ],
+      ""parameters"": [
+        {{
+          ""name"": ""booleanParam"",
+          ""type"": ""BooleanParameter"",
+          ""fields"": [
+            {{
+              ""name"": ""value"",
+              ""distribution"": ""Constant"",
+              ""value"": 1.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""floatParam"",
+          ""type"": ""FloatParameter"",
+          ""fields"": [
+            {{
+              ""name"": ""value"",
+              ""distribution"": ""AnimationCurve"",
+              ""value"": 0.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""integerParam"",
+          ""type"": ""IntegerParameter"",
+          ""fields"": [
+            {{
+              ""name"": ""value"",
+              ""distribution"": ""Uniform"",
+              ""value"": 0.0,
+              ""rangeMinimum"": -3.0,
+              ""rangeMaximum"": 7.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""vector2Param"",
+          ""type"": ""Vector2Parameter"",
+          ""fields"": [
+            {{
+              ""name"": ""x"",
+              ""distribution"": ""Constant"",
+              ""value"": 2.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""y"",
+              ""distribution"": ""Uniform"",
+              ""value"": 0.0,
+              ""rangeMinimum"": -4.0,
+              ""rangeMaximum"": 8.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""vector3Param"",
+          ""type"": ""Vector3Parameter"",
+          ""fields"": [
+            {{
+              ""name"": ""x"",
+              ""distribution"": ""Normal"",
+              ""value"": 0.0,
+              ""rangeMinimum"": -5.0,
+              ""rangeMaximum"": 9.0,
+              ""mean"": 4.0,
+              ""stdDev"": 2.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""y"",
+              ""distribution"": ""Constant"",
+              ""value"": 3.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""z"",
+              ""distribution"": ""AnimationCurve"",
+              ""value"": 0.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""vector4Param"",
+          ""type"": ""Vector4Parameter"",
+          ""fields"": [
+            {{
+              ""name"": ""x"",
+              ""distribution"": ""Normal"",
+              ""value"": 0.0,
+              ""rangeMinimum"": -5.0,
+              ""rangeMaximum"": 9.0,
+              ""mean"": 4.0,
+              ""stdDev"": 2.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""y"",
+              ""distribution"": ""Constant"",
+              ""value"": 3.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""z"",
+              ""distribution"": ""AnimationCurve"",
+              ""value"": 0.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }},
+            {{
+              ""name"": ""w"",
+              ""distribution"": ""Uniform"",
+              ""value"": 0.0,
+              ""rangeMinimum"": -12.0,
+              ""rangeMaximum"": 42.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 0
+            }}
+          ]
+        }},
+        {{
+          ""name"": ""colorRgbCategoricalParam"",
+          ""type"": ""ColorRgbCategoricalParameter"",
+          ""fields"": [
+            {{
+              ""name"": ""values"",
+              ""distribution"": ""Categorical"",
+              ""value"": 0.0,
+              ""rangeMinimum"": 0.0,
+              ""rangeMaximum"": 0.0,
+              ""mean"": 0.0,
+              ""stdDev"": 0.0,
+              ""categoricalParameterCount"": 3
+            }}
+          ]
+        }}
+      ]
+    }}
+  ]
+}}
+");
+            #endregion
+            var actualRandomizerJson = RemoveWhitespace(JsonConvert.SerializeObject(scenarioCompletedData));
+
+            Assert.AreEqual(expectedRandomizerJson, actualRandomizerJson);
+
+            Object.DestroyImmediate(emptyKeypointTemplate);
         }
 
         PerceptionCamera SetupPerceptionCamera()

com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/Resources/SampleScenarioConfiguration.json

     "randomSeed": 539662031
   },
   "randomizers": {
-    "groups": {
-      "RotationRandomizer": {
+    "randomizerGroups": [
+      {
+        "randomizerId": "RotationRandomizer",
+        },
+        "state": {
+          "enabled": true,
+          "canBeSwitchedByUser": true
         },
         "items": {
           "rotation": {
           }
         }
       }
-    }
+    ]
   }
 }

com.unity.perception/Editor/Randomization/Utilities/UIElementsEditorUtilities.cs

             if (originalField == null)
                 return null;
 
-            var tooltipAttribute = originalField.GetCustomAttributes(true)
-                .ToList().Find(att => att.GetType() == typeof(TooltipAttribute));
+            var attributes = originalField.GetCustomAttributes(true).ToList();
+            var tooltipAttribute = attributes.Find(att => att is TooltipAttribute);
+
+            var hideInspectorAttribute = attributes.Find(att => att is HideInInspector);
+            if (hideInspectorAttribute != null)
+                propertyField.style.display = DisplayStyle.None;
+
             return propertyField;
         }
     }

com.unity.perception/Editor/Randomization/Editors/RunInUnitySimulationWindow.cs

 using UnityEditor.Build.Reporting;
 using UnityEditor.UIElements;
 using UnityEngine;
+using UnityEngine.Perception;
+using UnityEngine.Perception.Analytics;
 using UnityEngine.Perception.Randomization.Samplers;
 using UnityEngine.Perception.Randomization.Scenarios;
 using UnityEngine.SceneManagement;
                 currentScenario = FindObjectOfType<ScenarioBase>()
             };
             var runGuid = Guid.NewGuid();
-            PerceptionEditorAnalytics.ReportRunInUnitySimulationStarted(
+            PerceptionAnalytics.ReportRunInUnitySimulationStarted(
                 runGuid,
                 m_RunParameters.totalIterations,
                 m_RunParameters.instanceCount,
             }
             catch (Exception e)
             {
-                PerceptionEditorAnalytics.ReportRunInUnitySimulationFailed(runGuid, e.Message);
+                PerceptionAnalytics.ReportRunInUnitySimulationFailed(runGuid, e.Message);
                 throw;
             }
             finally
             run.Execute();
 
             // Cleanup
-            PerceptionEditorAnalytics.ReportRunInUnitySimulationSucceeded(runGuid, run.executionId);
+            PerceptionAnalytics.ReportRunInUnitySimulationSucceeded(runGuid, run.executionId);
 
             // Set new Player Preferences
             PlayerPrefs.SetString("SimWindow/runName", m_RunParameters.runName);

com.unity.perception/Editor/Randomization/PropertyDrawers/AssetSourceDrawer.cs

-using Editor.Randomization.VisualElements.AssetSource;
+using UnityEditor.Perception.Randomization.VisualElements.AssetSource;
 using UnityEngine;
 using UnityEngine.Perception.Randomization;
 using UnityEngine.UIElements;

com.unity.perception/Editor/Randomization/VisualElements/AssetSource/AssetListElement.cs

 using System;
 using System.Collections;
-using UnityEditor;
-using UnityEditor.Perception.Randomization;
-namespace Editor.Randomization.VisualElements.AssetSource
+namespace UnityEditor.Perception.Randomization.VisualElements.AssetSource
 {
     class AssetListElement : VisualElement
     {

com.unity.perception/Editor/Randomization/VisualElements/AssetSource/AssetListItemElement.cs

 using UnityEditor.UIElements;
 using UnityEngine.UIElements;
 
-namespace Editor.Randomization.VisualElements.AssetSource
+namespace UnityEditor.Perception.Randomization.VisualElements.AssetSource
 {
     class AssetListItemElement : VisualElement
     {

com.unity.perception/Editor/Randomization/VisualElements/AssetSource/AssetSourceElement.cs

 using UnityEngine.Perception.Randomization;
 using UnityEngine.UIElements;
 
-namespace Editor.Randomization.VisualElements.AssetSource
+namespace UnityEditor.Perception.Randomization.VisualElements.AssetSource
 {
     class AssetSourceElement : VisualElement
     {

com.unity.perception/Runtime/GroundTruth/RenderPasses/CrossPipelinePasses/SemanticSegmentationCrossPipelinePass.cs

         const string k_ShaderName = "Perception/SemanticSegmentation";
         static readonly int k_LabelingId = Shader.PropertyToID("LabelingId");
 
-        static int s_LastFrameExecuted = -1;
+        int lastFrameExecuted = -1;
 
         SemanticSegmentationLabelConfig m_LabelConfig;
 
         protected override void ExecutePass(
             ScriptableRenderContext renderContext, CommandBuffer cmd, Camera camera, CullingResults cullingResult)
         {
-            if (s_LastFrameExecuted == Time.frameCount)
+            if (lastFrameExecuted == Time.frameCount)
-            s_LastFrameExecuted = Time.frameCount;
+            lastFrameExecuted = Time.frameCount;
             var renderList = CreateRendererListDesc(camera, cullingResult, "FirstPass", 0, m_OverrideMaterial, -1);
             cmd.ClearRenderTarget(true, true, m_LabelConfig.skyColor);
             DrawRendererList(renderContext, cmd, RendererList.Create(renderList));

com.unity.perception/Runtime/GroundTruth/RenderPasses/HdrpPasses/InstanceSegmentationPass.cs

         protected override void Execute(ScriptableRenderContext renderContext, CommandBuffer cmd, HDCamera hdCamera, CullingResults cullingResult)
         {
 #endif
+            if (targetCamera != hdCamera.camera)
+                return;
+
             CoreUtils.SetRenderTarget(cmd, targetTexture, ClearFlag.All);
             m_InstanceSegmentationCrossPipelinePass.Execute(renderContext, cmd, hdCamera.camera, cullingResult);
         }

com.unity.perception/Runtime/GroundTruth/RenderPasses/HdrpPasses/SemanticSegmentationPass.cs

         protected override void Execute(ScriptableRenderContext renderContext, CommandBuffer cmd, HDCamera hdCamera, CullingResults cullingResult)
         {
 #endif
+            if (targetCamera != hdCamera.camera)
+                return;
+
             CoreUtils.SetRenderTarget(cmd, targetTexture);
             m_SemanticSegmentationCrossPipelinePass.Execute(renderContext, cmd, hdCamera.camera, cullingResult);
         }

com.unity.perception/Runtime/GroundTruth/RenderPasses/UrpPasses/InstanceSegmentationUrpPass.cs

         {
             m_InstanceSegmentationPass = new InstanceSegmentationCrossPipelinePass(camera);
             ConfigureTarget(targetTexture, targetTexture.depthBuffer);
+            ConfigureClear(ClearFlag.None, Color.black);
             m_InstanceSegmentationPass.Setup();
         }
 

com.unity.perception/Runtime/GroundTruth/RenderPasses/UrpPasses/SemanticSegmentationUrpPass.cs

         {
             m_SemanticSegmentationCrossPipelinePass = new SemanticSegmentationCrossPipelinePass(camera, labelConfig);
             ConfigureTarget(targetTexture, targetTexture.depthBuffer);
+            ConfigureClear(ClearFlag.None, Color.black);
             m_SemanticSegmentationCrossPipelinePass.Setup();
         }
 

com.unity.perception/Runtime/GroundTruth/PerceptionCamera.cs

 
         static PerceptionCamera s_VisualizedPerceptionCamera;
 
+        /// <summary>
+        /// The number of capture-able frames that have been generated
+        /// </summary>
+#if UNITY_EDITOR
+        public static int captureFrameCount => Time.frameCount - 2;
+#else
+        public static int captureFrameCount => Time.frameCount - 1;
+#endif
+
         //TODO: Remove the Guid path when we have proper dataset merging in Unity Simulation and Thea
         internal string rgbDirectory { get; } = $"RGB{Guid.NewGuid()}";
         internal HUDPanel hudPanel;
         Ego m_EgoMarker;
         SensorHandle m_SensorHandle;
         Vector2 m_ScrollPosition;
+
+        internal Action<AsyncRequest<CaptureCamera.CaptureState>> RgbCaptureReadback = null;
 
 #if URP_PRESENT
         // only used to confirm that GroundTruthRendererFeature is present in URP
                 }
             };
 
+            AsyncRequest<CaptureCamera.CaptureState> request;
-            CaptureCamera.Capture(cam, colorFunctor, forceFlip: ForceFlip.None);
+            request = CaptureCamera.Capture(cam, colorFunctor, forceFlip: ForceFlip.None);
-            CaptureCamera.Capture(cam, colorFunctor, flipY: flipY);
+            request = CaptureCamera.Capture(cam, colorFunctor, flipY: flipY);
+            RgbCaptureReadback?.Invoke(request);
 
             Profiler.EndSample();
         }

com.unity.perception/Runtime/GroundTruth/Labelers/InstanceSegmentationLabeler.cs

         /// <inheritdoc/>
         protected override bool supportsVisualization => true;
 
-        static readonly string k_Directory = "InstanceSegmentation" + Guid.NewGuid().ToString();
+        string directory = "InstanceSegmentation" + Guid.NewGuid().ToString();
         const string k_FilePrefix = "Instance_";
 
         /// <summary>
             {
                 m_CurrentTexture = renderTexture;
 
-                m_InstancePath = $"{k_Directory}/{k_FilePrefix}{frameCount}.png";
-                var localPath = $"{Manager.Instance.GetDirectoryFor(k_Directory)}/{k_FilePrefix}{frameCount}.png";
+                m_InstancePath = $"{directory}/{k_FilePrefix}{frameCount}.png";
+                var localPath = $"{Manager.Instance.GetDirectoryFor(directory)}/{k_FilePrefix}{frameCount}.png";
 
                 var colors = new NativeArray<Color32>(data, Allocator.Persistent);
 

com.unity.perception/Runtime/GroundTruth/Labelers/KeypointLabeler.cs

             m_AnnotationDefinition = DatasetCapture.RegisterAnnotationDefinition("keypoints", new []{TemplateToJson(activeTemplate)},
                 "pixel coordinates of keypoints in a model, along with skeletal connectivity data", id: new Guid(annotationId));
 
+            visualizationEnabled = supportsVisualization;
+
             // Texture to use in case the template does not contain a texture for the joints or the skeletal connections
             m_MissingTexture = new Texture2D(1, 1);
 
             return json;
         }
     }
-}
+}

com.unity.perception/Runtime/Randomization/Samplers/ISampler.cs

         /// Validates that the sampler is configured properly
         /// </summary>
         void Validate();
+
+        /// <summary>
+        /// Check that the provided values adhere to the <see cref="minAllowed"/> and <see cref="maxAllowed"/> outputs for this sampler.
+        /// </summary>
+        public void CheckAgainstValidRange();
+
+        /// <summary>
+        /// Whether the provided <see cref="minAllowed"/> and <see cref="maxAllowed"/> values should be used to validate this sampler.
+        /// </summary>
+        public bool shouldCheckValidRange { get; set; }
+
+        /// <summary>
+        /// The smallest value this sampler should output
+        /// </summary>
+        public float minAllowed { get; set; }
+
+        /// <summary>
+        /// The largest value this sampler should output
+        /// </summary>
+        public float maxAllowed { get; set; }
     }
 }

com.unity.perception/Runtime/Randomization/Samplers/SamplerTypes/AnimationCurveSampler.cs

 using System;
+using UnityEngine.Assertions;
 
 namespace UnityEngine.Perception.Randomization.Samplers
 {
         /// </summary>
         [Tooltip("Probability distribution curve used for this sampler. The X axis corresponds to the values this sampler will pick from, and the Y axis corresponds to the relative probability of the values. The relative probabilities (Y axis) do not need to max out at 1 as only the shape of the curve matters. The Y values cannot however be negative.")]
         public AnimationCurve distributionCurve;
+
+        public void CheckAgainstValidRange()
+        {
+            throw new NotImplementedException();
+            //no range check currently performed for this sampler
+        }
+
+        ///<inheritdoc/>
+        [field: HideInInspector]
+        [field: SerializeField]
+        public float minAllowed { get; set; }
+        ///<inheritdoc/>
+        [field: HideInInspector]
+        [field: SerializeField]
+        public float maxAllowed { get; set; }
+        ///<inheritdoc/>
+        [field: HideInInspector]
+        [field: SerializeField]
+        public bool shouldCheckValidRange { get; set; }
 
         /// <summary>
         /// Number of samples used for integrating over the provided AnimationCurve.

com.unity.perception/Runtime/Randomization/Samplers/SamplerTypes/ConstantSampler.cs

 using System;
+using UnityEngine.Assertions;
 
 namespace UnityEngine.Perception.Randomization.Samplers
 {
         /// </summary>
         public float value;
 
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float minAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float maxAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public bool shouldCheckValidRange { get; set; }
+
         /// <summary>
         /// Constructs a ConstantSampler
         /// </summary>
         /// Constructs a new ConstantSampler
         /// </summary>
         /// <param name="value">The value from which samples will be generated</param>
-        public ConstantSampler(float value)
+        /// <param name="shouldCheckValidRange">Whether the provided <see cref="minAllowed"/> and <see cref="maxAllowed"/> values should be used to validate the <see cref="value"/> provided</param>
+        /// <param name="minAllowed">The smallest min value allowed for this range</param>
+        /// <param name="maxAllowed">The largest max value allowed for this range</param>
+        public ConstantSampler(float value, bool shouldCheckValidRange = false, float minAllowed = 0, float maxAllowed = 0)
+            this.shouldCheckValidRange = shouldCheckValidRange;
+            this.minAllowed = minAllowed;
+            this.maxAllowed = maxAllowed;
         }
 
         /// <summary>
         /// <summary>
         /// Validates that the sampler is configured properly
         /// </summary>
-        public void Validate() {}
+        public void Validate()
+        {
+            CheckAgainstValidRange();
+        }
+
+        public void CheckAgainstValidRange()
+        {
+            if (shouldCheckValidRange && (value < minAllowed || value > maxAllowed))
+            {
+                Debug.LogError($"The value a {GetType().Name} exceeds the allowed valid range. Clamping to valid range.");
+                value = Mathf.Clamp(value, minAllowed, maxAllowed);
+            }
+        }
     }
 }

com.unity.perception/Runtime/Randomization/Samplers/SamplerTypes/NormalSampler.cs

 using System;
+using UnityEngine.Assertions;
 
 namespace UnityEngine.Perception.Randomization.Samplers
 {
             mean = 0;
             standardDeviation = 1;
         }
+
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float minAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float maxAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public bool shouldCheckValidRange { get; set; }
 
         /// <summary>
         /// Constructs a normal distribution sampler
         /// <param name="mean">The mean of the normal distribution to sample from</param>
         /// <param name="standardDeviation">The standard deviation of the normal distribution to sample from</param>
+        /// <param name="shouldCheckValidRange">Whether the provided <see cref="minAllowed"/> and <see cref="maxAllowed"/> values should be used to validate the range provided with <see cref="minimum"/> and <see cref="maximum"/></param>
+        /// <param name="minAllowed">The smallest min value allowed for this range</param>
+        /// <param name="maxAllowed">The largest max value allowed for this range</param>
-            float min, float max, float mean, float standardDeviation)
+            float min, float max, float mean, float standardDeviation, bool shouldCheckValidRange = false, float minAllowed = 0, float maxAllowed = 0)
+            this.shouldCheckValidRange = shouldCheckValidRange;
+            this.minAllowed = minAllowed;
+            this.maxAllowed = maxAllowed;
         }
 
         /// <summary>
         public void Validate()
         {
             range.Validate();
+            CheckAgainstValidRange();
+        }
+
+        public void CheckAgainstValidRange()
+        {
+            if (shouldCheckValidRange && (range.minimum < minAllowed || range.maximum > maxAllowed))
+            {
+                Debug.LogError($"The provided min and max values for a {GetType().Name} exceed the allowed valid range. Clamping to valid range.");
+                range.minimum = Mathf.Clamp(range.minimum, minAllowed, maxAllowed);
+                range.maximum = Mathf.Clamp(range.maximum, minAllowed, maxAllowed);
+            }
         }
     }
 }

com.unity.perception/Runtime/Randomization/Samplers/SamplerTypes/UniformSampler.cs

 using System;
+using UnityEngine.Assertions;
 
 namespace UnityEngine.Perception.Randomization.Samplers
 {
         /// A range bounding the values generated by this sampler
         /// </summary>
         public FloatRange range;
+
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float minAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public float maxAllowed { get; set; }
+        ///<inheritdoc/>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public bool shouldCheckValidRange { get; set; }
 
         /// <summary>
         /// Constructs a UniformSampler
         /// </summary>
         /// <param name="min">The smallest value contained within the range</param>
         /// <param name="max">The largest value contained within the range</param>
-        public UniformSampler(float min, float max)
+        /// <param name="shouldCheckValidRange">Whether the provided <see cref="minAllowed"/> and <see cref="maxAllowed"/> values should be used to validate the range provided with <see cref="minimum"/> and <see cref="maximum"/></param>
+        /// <param name="minAllowed">The smallest min value allowed for this range</param>
+        /// <param name="maxAllowed">The largest max value allowed for this range</param>
+        public UniformSampler(float min, float max, bool shouldCheckValidRange = false, float minAllowed = 0, float maxAllowed = 0)
+            this.shouldCheckValidRange = shouldCheckValidRange;
+            this.minAllowed = minAllowed;
+            this.maxAllowed = maxAllowed;
         }
 
         /// <summary>
         public void Validate()
         {
             range.Validate();
+            CheckAgainstValidRange();
+        }
+
+        public void CheckAgainstValidRange()
+        {
+            if (shouldCheckValidRange && (range.minimum < minAllowed || range.maximum > maxAllowed))
+            {
+                Debug.LogError($"The provided min and max values for a {GetType().Name} exceed the allowed valid range. Clamping to valid range.");
+                range.minimum = Mathf.Clamp(range.minimum, minAllowed, maxAllowed);
+                range.maximum = Mathf.Clamp(range.maximum, minAllowed, maxAllowed);
+            }
         }
     }
 }

com.unity.perception/Runtime/Randomization/Randomizers/Randomizer.cs

         }
 
         /// <summary>
+        /// Some Randomizers should not be disabled by the user as they are critical to the project. E.g. We might want to mark this as false for a foreground objects placement randomizer in some projects
+        /// </summary>
+#if !SCENARIO_CONFIG_POWER_USER
+        [field: HideInInspector]
+#endif
+        [field: SerializeField]
+        public bool enabledStateCanBeSwitchedByUser { get; set; } = true;
+
+        /// <summary>
         /// Returns the scenario containing this Randomizer
         /// </summary>
         public ScenarioBase scenario => ScenarioBase.activeScenario;

com.unity.perception/Runtime/Randomization/Scenarios/PerceptionScenario.cs

         /// </summary>
         MetricDefinition m_IterationMetricDefinition;
 
-        /// <summary>
-        /// The scriptable render pipeline hook used to capture perception data skips the first frame of the simulation
-        /// when running locally, so this flag is used to track whether the first frame has been skipped yet.
-        /// </summary>
-        protected bool m_SkippedFirstFrame;
-
-        protected override bool isScenarioReadyToStart
-        {
-            get
-            {
-                if (!m_SkippedFirstFrame)
-                {
-                    m_SkippedFirstFrame = true;
-                    return false;
-                }
-                return true;
-            }
-        }
+        protected override bool isScenarioReadyToStart => PerceptionCamera.captureFrameCount >= 0;
 
         /// <inheritdoc/>
         protected override void OnAwake()

com.unity.perception/Runtime/Randomization/Scenarios/UnitySimulationScenario.cs

     public abstract class UnitySimulationScenario<T> : PerceptionScenario<T>
         where T : UnitySimulationScenarioConstants, new()
     {
-        /// <inheritdoc/>
-        protected override bool isScenarioReadyToStart
-        {
-            get
-            {
-                if (!Configuration.Instance.IsSimulationRunningInCloud() && !m_SkippedFirstFrame)
-                {
-                    m_SkippedFirstFrame = true;
-                    return false;
-                }
-                return true;
-            }
-        }
 
         /// <inheritdoc/>
         protected sealed override bool isScenarioComplete => currentIteration >= constants.totalIterations;

com.unity.perception/Runtime/Randomization/Scenarios/ScenarioBase.cs

 using System;
 using System.Collections.Generic;
 using System.IO;
+using System.Linq;
+using Unity.Simulation;
+using UnityEngine.Perception.Analytics;
+using UnityEngine.Perception.GroundTruth;
+using UnityEngine.SceneManagement;
 
 namespace UnityEngine.Perception.Randomization.Scenarios
 {
             {
                 foreach (var randomizer in activeRandomizers)
                     randomizer.ScenarioComplete();
+
                 OnComplete();
                 state = State.Idle;
                 OnIdle();

com.unity.perception/Runtime/Randomization/Scenarios/Serialization/JsonConverters.cs

                 if (itemValue is Parameter)
                     newObj["param"] = JObject.FromObject(itemValue);
                 else
-                    newObj["scalar"] = JObject.FromObject(itemValue);
+                    newObj["scalar"] = JObject.FromObject(itemValue, new JsonSerializer { NullValueHandling = NullValueHandling.Ignore });
                 output[itemKey] = newObj;
             }
             output.WriteTo(writer);
                 key = "normal";
             else
                 throw new TypeAccessException($"Cannot serialize type ${options.defaultSampler.GetType()}");
-            output[key] = JObject.FromObject(options.defaultSampler);
+            output[key] = JObject.FromObject(options.defaultSampler, new JsonSerializer { NullValueHandling = NullValueHandling.Ignore });
             output.WriteTo(writer);
         }
 
             if (value.ContainsKey("str"))
                 scalar.value = new StringScalarValue { str = value["str"].Value<string>() };
             else if (value.ContainsKey("num"))
-                scalar.value  = new DoubleScalarValue { num = value["num"].Value<double>() };
+            {
+                Limits limits = null;
+                if (value.ContainsKey("limits"))
+                {
+                    limits = value["limits"].ToObject<Limits>();
+                }
+                scalar.value = new DoubleScalarValue { num = value["num"].Value<double>(), limits = limits};
+            }
+
             return scalar;
         }
     }

com.unity.perception/Runtime/Randomization/Scenarios/Serialization/SerializationStructures.cs

     #region GroupedObjects
     class TemplateConfigurationOptions
     {
-        public Dictionary<string, Group> groups = new Dictionary<string, Group>();
+        public List<Group> randomizerGroups = new List<Group>();
     }
 
     class StandardMetadata
         public string imageLink = string.Empty;
     }
 
+    class RandomizerStateData
+    {
+        public bool enabled;
+        public bool canBeSwitchedByUser;
+    }
+
+    class Limits
+    {
+        public double min;
+        public double max;
+    }
+
+        public string randomizerId;
+        public RandomizerStateData state = null;
         [JsonConverter(typeof(GroupItemsConverter))]
         public Dictionary<string, IGroupItem> items = new Dictionary<string, IGroupItem>();
     }
     {
         public double min;
         public double max;
+        public Limits limits;
     }
 
     class NormalSampler : ISamplerOption
         public double mean;
         public double stddev;
+        public Limits limits;
+        public Limits limits;
     }
     #endregion
 
     class DoubleScalarValue : IScalarValue
     {
         public double num;
+        public Limits limits;
     }
 
     class BooleanScalarValue : IScalarValue

com.unity.perception/Runtime/Randomization/Scenarios/Serialization/ScenarioSerializer.cs

         {
             return new TemplateConfigurationOptions
             {
-                groups = SerializeRandomizers(scenario.randomizers)
+                randomizerGroups = SerializeRandomizers(scenario.randomizers)
-        static Dictionary<string, Group> SerializeRandomizers(IEnumerable<Randomizer> randomizers)
+        static List<Group> SerializeRandomizers(IEnumerable<Randomizer> randomizers)
-            var serializedRandomizers = new Dictionary<string, Group>();
+            var serializedRandomizers = new List<Group>();
-                if (randomizerData.items.Count == 0)
+                if (randomizerData.items.Count == 0 && !randomizerData.state.canBeSwitchedByUser)
-                serializedRandomizers.Add(randomizer.GetType().Name, randomizerData);
+
+                serializedRandomizers.Add(randomizerData);
             }
             return serializedRandomizers;
         }
             var randomizerData = new Group();
+            randomizerData.state = new RandomizerStateData
+            {
+                enabled = randomizer.enabled,
+                canBeSwitchedByUser = randomizer.enabledStateCanBeSwitchedByUser
+            };
+
+            randomizerData.randomizerId = randomizer.GetType().Name;
             foreach (var field in fields)
             {
                 if (field.FieldType.IsSubclassOf(typeof(Randomization.Parameters.Parameter)))
             if (sampler is Samplers.ConstantSampler constantSampler)
                 samplerData.defaultSampler = new ConstantSampler
                 {
-                    value = constantSampler.value
+                    value = constantSampler.value,
+                    limits = constantSampler.shouldCheckValidRange? new Limits {
+                        min = constantSampler.minAllowed,
+                        max = constantSampler.maxAllowed,
+                    } : null
-                    max = uniformSampler.range.maximum
+                    max = uniformSampler.range.maximum,
+                    limits = uniformSampler.shouldCheckValidRange? new Limits {
+                        min = uniformSampler.minAllowed,
+                        max = uniformSampler.maxAllowed,
+                    } : null
                 };
             else if (sampler is Samplers.NormalSampler normalSampler)
                 samplerData.defaultSampler = new NormalSampler
                     mean = normalSampler.mean,
-                    stddev = normalSampler.standardDeviation
+                    stddev = normalSampler.standardDeviation,
+                    limits = normalSampler.shouldCheckValidRange? new Limits {
+                        min = normalSampler.minAllowed,
+                        max = normalSampler.maxAllowed,
+                    } : null
                 };
             else
                 throw new ArgumentException($"Invalid sampler type ({sampler.GetType()})");
             if (field.FieldType == typeof(bool))
                 return new BooleanScalarValue { boolean = (bool)field.GetValue(obj) };
             if (field.FieldType == typeof(float) || field.FieldType == typeof(double) || field.FieldType == typeof(int))
-                return new DoubleScalarValue { num = Convert.ToDouble(field.GetValue(obj)) };
+            {
+                var minAllowed = 0f;
+                var maxAllowed = 0f;
+                var shouldCheckValidRange = false;
+                var rangeAttributes = field.GetCustomAttributes(typeof(RangeAttribute));
+                if (rangeAttributes.Any())
+                {
+                    var rangeAttribute = (RangeAttribute)rangeAttributes.First();
+                    minAllowed = rangeAttribute.min;
+                    maxAllowed = rangeAttribute.max;
+                    shouldCheckValidRange = true;
+                }
+                return new DoubleScalarValue
+                {
+                    num = Convert.ToDouble(field.GetValue(obj)),
+                    limits = shouldCheckValidRange? new Limits {
+                        min = minAllowed,
+                        max = maxAllowed,
+                    } : null
+                };
+            }
+
             return null;
         }
         #endregion
 
         static void DeserializeTemplateIntoScenario(ScenarioBase scenario, TemplateConfigurationOptions template)
         {
-            DeserializeRandomizers(scenario.randomizers, template.groups);
+            DeserializeRandomizers(scenario.randomizers, template.randomizerGroups);
-        static void DeserializeRandomizers(IEnumerable<Randomizer> randomizers, Dictionary<string, Group> groups)
+        static void DeserializeRandomizers(IEnumerable<Randomizer> randomizers, List<Group> groups)
-            foreach (var randomizerPair in groups)
+            foreach (var randomizerData in groups)
-                if (!randomizerTypeMap.ContainsKey(randomizerPair.Key))
+                if (!randomizerTypeMap.ContainsKey(randomizerData.randomizerId))
-                var randomizer = randomizerTypeMap[randomizerPair.Key];
-                DeserializeRandomizer(randomizer, randomizerPair.Value);
+                var randomizer = randomizerTypeMap[randomizerData.randomizerId];
+                DeserializeRandomizer(randomizer, randomizerData);
+            if (randomizerData.state != null)
+            {
+                randomizer.enabled = randomizerData.state.enabled;
+                randomizer.enabledStateCanBeSwitchedByUser = randomizerData.state.canBeSwitchedByUser;
+            }
+
             foreach (var pair in randomizerData.items)
             {
                 var field = randomizer.GetType().GetField(pair.Key);
             if (samplerOption is ConstantSampler constantSampler)
                 return new Samplers.ConstantSampler
                 {
-                    value = (float)constantSampler.value
+                    value = (float)constantSampler.value,
+                    minAllowed = constantSampler.limits != null ? (float)constantSampler.limits.min : 0,
+                    maxAllowed = constantSampler.limits != null ? (float)constantSampler.limits.max : 0,
+                    shouldCheckValidRange = constantSampler.limits != null
                 };
             if (samplerOption is UniformSampler uniformSampler)
                 return new Samplers.UniformSampler
                         minimum = (float)uniformSampler.min,
-                        maximum = (float)uniformSampler.max
-                    }
+                        maximum = (float)uniformSampler.max,
+                    },
+                    minAllowed = uniformSampler.limits != null ? (float)uniformSampler.limits.min : 0,
+                    maxAllowed = uniformSampler.limits != null ? (float)uniformSampler.limits.max : 0,
+                    shouldCheckValidRange = uniformSampler.limits != null
                 };
             if (samplerOption is NormalSampler normalSampler)
                 return new Samplers.NormalSampler
                         maximum = (float)normalSampler.max
                     },
                     mean = (float)normalSampler.mean,
-                    standardDeviation = (float)normalSampler.stddev
+                    standardDeviation = (float)normalSampler.stddev,
+                    minAllowed = normalSampler.limits != null ? (float)normalSampler.limits.min : 0,
+                    maxAllowed = normalSampler.limits != null ? (float)normalSampler.limits.max : 0,
+                    shouldCheckValidRange = normalSampler.limits != null
                 };
             throw new ArgumentException($"Cannot deserialize unsupported sampler type {samplerOption.GetType()}");
         }
+            var rangeAttributes = field.GetCustomAttributes(typeof(RangeAttribute));
+            RangeAttribute rangeAttribute = null;
+
+            if (rangeAttributes.Any())
+            {
+                rangeAttribute = (RangeAttribute) rangeAttributes.First();
+            }
+
+            var readScalar = ReadScalarValue(obj, scalar);
+            var tolerance = 0.00001f;
+
+            if (readScalar.Item1 is double num)
+            {
+                if (rangeAttribute != null)
+                {
+                    if (readScalar.Item2 != null &&
+                        (Math.Abs(rangeAttribute.min - readScalar.Item2.min) > tolerance || Math.Abs(rangeAttribute.max - readScalar.Item2.max) > tolerance))
+                    {
+                        //the field has a range attribute and the json has a limits block for this field, but the numbers don't match
+                        Debug.LogError($"The limits provided in the Scenario JSON for the field \"{field.Name}\" of \"{obj.GetType().Name}\" do not match this field's range set in the code. Ranges for scalar fields can only be set in code using the Range attribute and not from the Scenario JSON.");
+                    }
+                    else if (readScalar.Item2 == null)
+                    {
+                        //the field has a range attribute but the json has no limits block for this field
+                        Debug.LogError($"The provided Scenario JSON specifies limits for the field \"{field.Name}\" of \"{obj.GetType().Name}\", while the field has no Range attribute in the code. Ranges for scalar fields can only be set in code using the Range attribute and not from the Scenario JSON.");
+                    }
+
+                    if (num < rangeAttribute.min || num > rangeAttribute.max)
+                    {
+                        Debug.LogError($"The provided value for the field \"{field.Name}\" of \"{obj.GetType().Name}\" exceeds the allowed valid range. Clamping to valid range.");
+                        var clamped = Mathf.Clamp((float)num, rangeAttribute.min, rangeAttribute.max);
+                        field.SetValue(obj, Convert.ChangeType(clamped, field.FieldType));
+                    }
+                    else
+                        field.SetValue(obj, Convert.ChangeType(readScalar.Item1, field.FieldType));
+
+                }
+                else
+                {
+                    if (readScalar.Item2 != null)
+                        //the field does not have a range attribute but the json has a limits block for this field
+                        Debug.LogError($"The provided Scenario JSON specifies limits for the field \"{field.Name}\" of \"{obj.GetType().Name}\", but the field has no Range attribute in code. Ranges for scalar fields can only be set in code using the Range attribute and not from the Scenario JSON.");
+
+                    field.SetValue(obj, Convert.ChangeType(readScalar.Item1, field.FieldType));
+                }
+
+            }
+            else
+            {
+                field.SetValue(obj, Convert.ChangeType(readScalar.Item1, field.FieldType));
+            }
+        }
+
+        static (object, Limits) ReadScalarValue(object obj, Scalar scalar)
+        {
+            Limits limits = null;
+            {
+                limits = doubleValue.limits;
+            }
-            field.SetValue(obj, Convert.ChangeType(value, field.FieldType));
+
+            return (value, limits);
         }
         #endregion
 

com.unity.perception/CHANGELOG.md

 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
-## Unreleased
+## [0.9.0-preview.2] - 2021-11-01
+
+Scenario JSON configurations that were generated using previous versions will need to be manually updated due to updates to serialization and deserialization of scenarios.
-The user can now choose the base folder location to store their generated datasets.
+Added support for multiple cameras capturing simultaneously (visualization not yet supported)
-Added the AssetSource class for loading assets from generic sources inside randomizers.
+Users can now delay the current iteration for one frame from within randomizers by calling the `DelayIteration` function of the active scenario.
+Added new configuration options to the Scenario JSON configuration. These include a `limits` block on numerical Scalars and Samplers to denote a valid range, and a `state` block on the Randomizers for enabling/disabling them from the config.
+
+Added the AssetSource class for loading assets from generic sources inside randomizers.
+
-Users can now delay the current iteration for one frame from within randomizers by calling the `DelayIteration` function of the active scenario.
+Added usage analytics for the scenario configuration. For more information, refer to the Release Notes for package version 0.9.0.preview.2.
-Changed the JSON serialization key of Normal Sampler's standard deviation property from "standardDeviation" to "stddev". Scneario JSON configurations that were generated using previous versions will need to be manually updated to reflect this change.
+Upgraded capture package dependency to 0.0.10-preview.24. This fixes an issue where RGB images were blank on OSX when MSAA was enabled & post-processing was disabled.
+
+Changed the JSON serialization key of Normal Sampler's standard deviation property from "standardDeviation" to "stddev".
+
+Changed the JSON serialization of Randomizers in a scenario to an array of groups rather than a dictionary.
 
 ### Deprecated
 

com.unity.perception/Documentation~/SetupSteps.md

 
 Note that although the Perception package is compatible with both URP and HDRP, Unity Simulation currently only supports URP projects, therefore a URP project is recommended. 
 
-If you want a specific version of the package, append the version to the end of the "git URL". Ex. `com.unity.perception@0.8.0-preview.1`
+If you want a specific version of the package, append the version to the end of the "git URL". Ex. `com.unity.perception@0.8.0-preview.4`
 
 To install from a local clone of the repository, see [installing a local package](https://docs.unity3d.com/Manual/upm-ui-local.html) in the Unity manual.

com.unity.perception/Documentation~/Tutorial/Phase3.md

 * [Step 1: Setup Unity Account, Unity Simulation, and Cloud Project](#step-1)
 * [Step 2: Run Project on Unity Simulation](#step-2)
 * [Step 3: Keep Track of Your Runs Using the Unity Simulation Command-Line Interface](#step-3)
-* [Step 4: Analyze the Dataset using Dataset Insights](#step-4)
 
 ### <a name="step-1">Step 1: Setup Unity Account, Unity Simulation, and Cloud Project</a> 
 
  
  * **:green_circle: Action**: Open the manifest file to check it. Make sure there are links to various types of output and check a few of the links to see if they work.
 
-
-### <a name="step-4">Step 4: Analyze the Dataset using Dataset Insights</a> 
-
- In order to download the actual data from your run, we will now use Dataset Insights again. This time though, we will utilize some of the lines that were commented in our previous use with locally generated data.
-
- * **:green_circle: Action**: Open the Dataset Insights Jupyter notebook again, using the command below:
- 
- `docker run -p 8888:8888 -v <download path>/data:/data -t unitytechnologies/datasetinsights:latest`
-
-> :information_source: If you get an error about the format of the command, try the command again **with quotation marks** around the folder mapping argument, i.e. `"<download path>/data:/data"`.
-
-In the above command, replace `<download path>` with the location on your computer in which you wish to download your data.
-
-Once the Docker image is running, the rest of the workflow is quite similar to what we did in Phase 1, with certain differences caused by the need to download the data from Unity Simulation.
-
-* **:green_circle: Action**: Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook.
-* **:green_circle: Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
-* **:green_circle: Action**: In the `data_root = /data/<GUID>` line, the `<GUID>` part will be the location inside your `<download path>` where the data will be downloaded. Therefore, you can just remove it so as to have data downloaded directly to the path you previously specified:
-
-<p align="center">
-<img src="Images/di_usim_1.png" width="900"/>
-</p>
-
-The next few lines of code pertain to setting up your notebook for downloading data from Unity Simulation. 
-
-* **:green_circle: Action**: In the block of code titled "Unity Simulation [Optional]", uncomment the lines that assign values to variables, and insert the correct values, based on information from your Unity Simulation run. 
-
-We have previously learned how to obtain the `run_execution_id` and `project_id`. You can remove the value already present for `annotation_definition_id` and leave it blank. What's left is the `access_token`.
-
-* **:green_circle: Action**: Return to your command-line interface and run the `usim inspect auth` command.
-
-MacOS:
- `USimCLI/mac/usim inspect auth`
-
-If you receive errors regarding authentication, your token might have timed out. Repeat the login step (`usim login auth`) to login again and fix this issue.
-
-A sample output from `usim inspect auth` will look like below:
-
-```
-Protect your credentials. They may be used to impersonate your requests.
-access token: Bearer 0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp
-expires in: 2:00:05.236227
-expired: False
-refresh token: FW4c3YRD4IXi6qQHv3Y9W-rwg59K7k0Te9myKe7Zo6M003f.k4Dqo0tuoBdf-ncm003fX2RAHQ
-updated: 2020-10-02 14:50:11.412979
-```
-
-The `access_token` you need for your Dataset Insights notebook is the access token shown by the above command, minus the `'Bearer '` part. So, in this case, we should input `0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp` in the notebook. 
-
-* **:green_circle: Action**: Copy the access token excluding the `'Bearer '` part to the corresponding field in the Dataset Insights notebook.
-
-Once you have entered all the information, the block of code should look like the screenshot below (the actual values you input will be different):
-
-<p align="center">
-<img src="Images/di_usim_2.png" width="800"/>
-</p>
-
-
-* **:green_circle: Action**: Continue to the next code block and run it to download all the metadata files from the generated dataset. This includes JSON files and logs but does not include images (which will be downloaded later). 
-
-You will see a progress bar while the data downloads:
-
-<p align="center">
-<img src="Images/di_usim_3.png" width="800"/>
-</p>
-
-
-The next couple of code blocks (under "Load dataset metadata") analyze the downloaded metadata and display a table containing annotation-definition-ids for the various metrics defined in the dataset.
-
-* **:green_circle: Action**: Once you reach the code block titled "Built-in Statistics", make sure the value assigned to the field `rendered_object_info_definition_id` matches the id displayed for this metric in the table output by the code block immediately before it. The screenshot below demonstrates this (note that your ids might differ from the ones here):
-
-<p align="center">
-<img src="Images/di_usim_4.png" width="800"/>
-</p>
-
-Follow the rest of the steps inside the notebook to generate a variety of plots and stats. Keep in mind that this notebook is provided just as an example, and you can modify and extend it according to your own needs using the tools provided by the [Dataset Insights framework](https://datasetinsights.readthedocs.io/en/latest/).
+Once the run execution is complete, you can use Unity's [Datasets Insights](https://github.com/Unity-Technologies/datasetinsights) framework to download your dataset and analyze it. This is covered in the Unity Simulation section of the [Dataset Insights](DatasetInsights.md) guide.
 
 The next step in this workflow, which is out of the scope of this tutorial, is to train an object-detection model using our synthetic dataset. It is important to note that the 1000 large dataset we generated here is probably not sufficiently large for training most models. We chose this number here so that the Unity Simulation run would finish quickly, allowing us to move on to learning how to analyze the statistics of the dataset. In order to generate data for training, we recommend a minimum dataset size of around 50,000 captures with a large degree of randomization. 
 

com.unity.perception/Documentation~/Tutorial/Phase2.md

 }
 ```
 
-Notice how we now utilize the `SetIntensity` fucntion of `MyLightRandomizerTag` components of the tagged objects, instead of directly setting the intensity of the `Light` components.
+Notice how we now utilize the `SetIntensity` function of `MyLightRandomizerTag` components of the tagged objects, instead of directly setting the intensity of the `Light` components.
 
 * **:green_circle: Action**: Run your simulation, then pause it. Go to the _**Scene**_ view and inspect the color and intensity of each of the lights. Try turning each on and off to see how they affect the current frame. 
 

com.unity.perception/Documentation~/Tutorial/Phase1.md

 # Perception Tutorial
 ## Phase 1: Setup and Basic Randomizations
 
-In this phase of the Perception tutorial, you will start from downloading and installing Unity Editor and the Perception package. You will then use our sample assets and provided components to easily generate a synthetic dataset for training an object-detection model. 
+In this phase of the Perception tutorial, you will start by downloading and installing Unity Editor and the Perception package. You will then use our sample assets and provided components to easily generate a synthetic dataset for training an object-detection model. 
-Through-out the tutorial, lines starting with bullet points followed by **":green_circle: Action:"** denote the individual actions you will need to perform in order to progress through the tutorial. This is while the rest of the text will provide additional context and explanation around the actions. If in a hurry, you can just follow the actions!
+Throughout the tutorial, lines starting with bullet points followed by **":green_circle: Action:"** denote the individual actions you will need to perform in order to progress through the tutorial. This is while the rest of the text will provide additional context and explanation around the actions. If in a hurry, you can just follow the actions!
 
 Steps included in this phase of the tutorial:
 
 * [Step 5: Set Up Background Randomizers](#step-5)
 * [Step 6: Set Up Foreground Randomizers](#step-6)
 * [Step 7: Inspect Generated Synthetic Data](#step-7)
-* [Step 8: Verify Data Using Dataset Insights](#step-8)
+* [Step 8: Explore and Visualize Your Dataset](#step-8)
 
 
 > :information_source: If you face any problems while following this tutorial, please create a post on the **[Unity Computer Vision forum](https://forum.unity.com/forums/computer-vision.626/)** or the **[GitHub issues](https://github.com/Unity-Technologies/com.unity.perception/issues)** page and include as much detail as possible.
 
 * **:green_circle: Action**: Make sure the _**Linux Build Support (Mono)**_ and _**Visual Studio**_ installation options are checked when selecting modules during installation.
 
-When you first run Unity, you will be asked to open an existing project, or create a new one. 
+When you first run Unity, you will be asked to open an existing project or create a new one. 
-* **:green_circle: Action**: Open Unity and create a new project using the Universal Render Pipeline. Name your new project _**Perception Tutorial**_, and specify a desired location as shown below. 
+* **:green_circle: Action**: Open Unity and create a new project using the Universal Render Pipeline. Name your new project _**Perception Tutorial**_, and specify the desired location as shown below. 
 
 <p align="center">
 <img src="Images/create_new_project.png" align="center" width="800"/>
 * **:green_circle: Action**: Click on the _**+**_ sign at the top-left corner of the _**Package Manager**_ window and then choose the option _**Add package from git URL...**_. 
 * **:green_circle: Action**: Enter the address `com.unity.perception` and click _**Add**_.
 
-> :information_source: If you would like to install a specific version of the package, you can append the version to the end of the url. For example `com.unity.perception@0.8.0-preview.1`. For this tutorial, **we do not need to add a version**. You can also install the package from a local clone of the Perception repository. More information on installing local packages is available [here](https://docs.unity3d.com/Manual/upm-ui-local.html).
+> :information_source: If you would like to install a specific version of the package, you can append the version to the end of the url. For example `com.unity.perception@0.8.0-preview.4`. For this tutorial, **we do not need to add a version**. You can also install the package from a local clone of the Perception repository. More information on installing local packages is available [here](https://docs.unity3d.com/Manual/upm-ui-local.html).
 
 It will take some time for the manager to download and import the package. Once the operation finishes, you will see the newly downloaded Perception package automatically selected in the _**Package Manager**_, as depicted below:
 
 <img src="Images/perc_comp.png" width="400"/>
 </p>
 
-If you hover your mouse pointer over each of the fields shown (e.g. `Simulation Delta Time`), you will see a tooltip popup with an explanation on what the item controls.
+If you hover your mouse pointer over each of the fields shown (e.g. `Simulation Delta Time`), you will see a tooltip popup with an explanation of what the item controls.
-As seen in the UI for `Perception Camera`, the list of `Camera Labelers` is currently empty. For each type of ground-truth you wish to generate along-side your captured frames (e.g. 2D bounding boxes around objects), you will need to add a corresponding `Camera Labeler` to this list. 
+As seen in the UI for `Perception Camera`, the list of `Camera Labelers` is currently empty. For each type of ground-truth you wish to generate alongside your captured frames (e.g. 2D bounding boxes around objects), you will need to add a corresponding `Camera Labeler` to this list. 
-To speed-up your workflow, the Perception package comes with seven common Labelers for object-detection and human keypoint labeling tasks; however, if you are comfortable with code, you can also add your own custom Labelers. The Labelers that come with the Perception package cover **keypoint labeling, 3D bounding boxes, 2D bounding boxes, object counts, object information (pixel counts and ids), instance segmentation, and semantic segmentation**. We will use four of these in this tutorial.
+To speed up your workflow, the Perception package comes with seven common Labelers for object-detection and human keypoint labeling tasks; however, if you are comfortable with code, you can also add your own custom Labelers. The Labelers that come with the Perception package cover **keypoint labeling, 3D bounding boxes, 2D bounding boxes, object counts, object information (pixel counts and ids), instance segmentation, and semantic segmentation**. We will use four of these in this tutorial.
 
 * **:green_circle: Action**: Click on the _**+**_ button at the bottom right corner of the empty labeler list and select `BoundingBox2DLabeler`.
 * **:green_circle: Action**: Repeat the above step to add `ObjectCountLabeler`, `RenderedObjectInfoLabeler`, `SemanticSegmentationLabeler`. 
 
 Click on this asset to bring up its _**Inspector**_ view. In there, you can specify the labels that this config will keep track of. You can type in labels, add any labels defined in the project (through being added to prefabs), and import/export this label config as a JSON file. A new label config like this one contains an empty list of labels.
 
-In this tutorial, we will generate synthetic data intended for detecting 10 everyday grocery items. These grocery items were imported into your project when you imported the tutorial files from the _**Package Manager**_, and are located in the folder `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
+In this tutorial, we will generate synthetic data intended for detecting 10 everyday grocery items. These grocery items were imported into your project when you imported the tutorial files from the _**Package Manager**_, and are located in the folder `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
 
 The label configuration we have created (`TutorialIdLabelConfig`) is of type `IdLabelConfig`, and is compatible with three of the four labelers we have attached to our `Perception Camera`. This type of label configuration carries a unique numerical ID for each label. However, `SemanticSegmentationLabeler` requires a different kind of label configuration which includes unique colors for each label instead of numerical IDs. This is because the output of this labeler is a set of images in which each visible foreground object is painted in a unique color.
 
 <img src="Images/pclabelconfigsadded.png" width="400"/>
 </p>
 
-It is now time to assign labels to the objects that are supposed to be detected by an eventual object-detection model, and add those labels to both of the label configurations we have created. As mentioned above, these objects are located at `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`. 
+It is now time to assign labels to the objects that are supposed to be detected by an eventual object-detection model, and add those labels to both of the label configurations we have created. As mentioned above, these objects are located at `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`. 
-* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`
+* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`
 * **:green_circle: Action**: Double click the file named `drink_whippingcream_lucerne.prefab` to open the Prefab asset. 
 
 When you open the Prefab asset, you will see the object shown in the Scene tab and its components shown on the right side of the editor, in the _**Inspector**_ tab:
 </p>
 
-The Prefab contains a number of components, including a `Transform`, a `Mesh Filter`, a `Mesh Renderer` and a `Labeling` component (highlighted in the image above). While the first three of these are common Unity components, the fourth one is specific to the Perception package, and is used for assigning labels to objects. You can see here that the Prefab has one label already added, displayed in the list of `Added Labels`. The UI here provides a multitude of ways for you to assign labels to the object. You can either choose to have the asset automatically labeled (by enabling `Use Automatic Labeling`), or add labels manually. In case of automatic labeling, you can choose from a number of labeling schemes, e.g. the asset's name or folder name. If you go the manual route, you can type in labels, add labels from any of the label configurations included in the project, or add from lists of suggested labels based on the Prefab's name and path. 
+The Prefab contains a number of components, including a `Transform`, a `Mesh Filter`, a `Mesh Renderer` and a `Labeling` component (highlighted in the image above). While the first three of these are common Unity components, the fourth one is specific to the Perception package and is used for assigning labels to objects. You can see here that the Prefab has one label already added, displayed in the list of `Added Labels`. The UI here provides a multitude of ways for you to assign labels to the object. You can either choose to have the asset automatically labeled (by enabling `Use Automatic Labeling`), or add labels manually. In case of automatic labeling, you can choose from a number of labeling schemes, e.g. the asset's name or folder name. If you go the manual route, you can type in labels, add labels from any of the label configurations included in the project, or add from lists of suggested labels based on the Prefab's name and path. 
-Note that each object can have multiple labels assigned, and thus appear as different objects to Labelers with different label configurations. For instance, you may want your semantic segmentation Labeler to detect all cream cartons as `dairy_product`, while your bounding box Labeler still distinguishes between different types of dairy product. To achieve this, you can add a `dairy_product` label to all your dairy products, and then in your label configuration for semantic segmentation, only add the `dairy_product` label, and not any specific products or brand names.
+Note that each object can have multiple labels assigned, and thus appear as different objects to Labelers with different label configurations. For instance, you may want your semantic segmentation Labeler to detect all cream cartons as `dairy_product`, while your Bounding Box Labeler still distinguishes between different types of dairy products. To achieve this, you can add a `dairy_product` label to all your dairy products, and then in your label configuration for semantic segmentation, only add the `dairy_product` label, and not any specific products or brand names.
 
 For this tutorial, we have already prepared the foreground Prefabs for you and added the `Labeling` component to all of them. These Prefabs were based on 3D scans of the actual grocery items. If you are making your own Prefabs, you can easily add a `Labeling` component to them using the _**Add Component**_ button visible in the bottom right corner of the screenshot above.
 
 
-* **:green_circle: Action**: Select **all the files** inside the `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs` folder.
+* **:green_circle: Action**: Select **all the files** inside the `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs` folder.
 * **:green_circle: Action**: From the _**Inspector**_ tab, enable `Use Automatic Labeling for All Selected Items`, and then select `Use asset name` as the labeling scheme.
 
 <p align="center">
 
 > :information_source: Since we used automatic labels here and added them to our configurations, we are confident that the labels in the configurations match the labels of our objects. In cases where you decide to add manual labels to objects and configurations, make sure you use the exact same labels, otherwise, the objects for which a matching label is not found in your configurations will not be detected by the Labelers that are using those configurations.
 
-Now that we have labelled all our foreground objects and setup our label configurations, let's briefly test things.
+Now that we have labeled all our foreground objects and set up our label configurations, let's briefly test things.
-* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
+* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
 * **:green_circle: Action**: Drag and drop any of the Prefabs inside this folder into the Scene.
 * **:green_circle: Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
 
 
 As mentioned earlier, one of the core ingredients of the perception workflow is the randomization of various aspects of the simulation, in order to introduce sufficient variation into the generated data. 
 
-To start randomizing your simulation you will first need to add a `Scenario` to your scene. Scenarios control the execution flow of your simulation by coordinating all `Randomizer` components added to them. The Perception package comes with a useful set of Randomizers that let you quickly place your foreground objects in the Scene, generate varied backgrounds, as well as randomize various parameters of the simulation over time, including things such as position, scale, and rotation of objects, number of objects within the camera's view, and so on. Randomizers achieve this through coordinating a number of `Parameter`s, which essentially define the most granular randomization behaviors. For instance, for continuous variable types such as floats, vectors, and colors, Parameters can define the range and sampling distribution for randomization. This is while another class of Parameters let you randomly select one out of a number of categorical options.  
+To start randomizing your simulation you will first need to add a `Scenario` to your scene. Scenarios control the execution flow of your simulation by coordinating all `Randomizer` components added to them. The Perception package comes with a useful set of Randomizers that let you quickly place your foreground objects in the Scene, generate varied backgrounds, as well as randomize various parameters of the simulation over time such as position, scale, and rotation of objects, number of objects within the camera's view, and so on. Randomizers achieve this through coordinating a number of `Parameter`s, which essentially define the most granular randomization behaviors. For instance, for continuous variable types such as floats, vectors, and colors, Parameters can define the range and sampling distribution for randomization. While another class of Parameters let you randomly select one out of a number of categorical options.  
 
 To summarize, a sample `Scenario` could look like this:
 
 
 This Randomizer uses Poisson-Disk sampling to select random positions from a given area, and spawn copies of randomly selected Prefabs (from a given list) at the chosen positions. We will use this component to generate a background that will act as a distraction for our eventual object-detection machine learning model.
 
-* **:green_circle: Action**: Click _**Add Folder**_, and from the file explorer window that opens, choose the folder `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
+* **:green_circle: Action**: Click _**Add Folder**_, and from the file explorer window that opens, choose the folder `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Background Objects/Prefabs`.
 
 The background Prefabs are primitive shapes devoid of color or texture. Later Randomizers will take care of those aspects. 
 
 
 `TextureRandomizer` will have the task of attaching random textures to our colorless background objects at each Iteration of the Scenario. Similarly, `HueOffsetRandomizer` will alter the color of the objects, and `RotationRandomizer` will give the objects a new random rotation each Iteration. 
 
-* **:green_circle: Action**: In the UI snippet for `TextureRandomizer`, click _**Add Folder**_ and choose `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Background Textures`. 
+* **:green_circle: Action**: In the UI snippet for `TextureRandomizer`, click _**Add Folder**_ and choose `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Background Textures`. 
 
 * **:green_circle: Action**: In the UI snippet for `RotationRandomizer`, verify that all the minimum values for the three ranges are `0` and that maximum values are `360`. 
 
 <img src="Images/all_back_rands.png" width = "400"/>
 </p>
 
-There is one more important thing left to do, in order to make sure all the above Randomizers operate as expected. Since `BackgroundObjectPlacementRandomizer` spawns objects, it already knows which objects in the Scene it is dealing with; however, the rest of the Randomizers  we added are not yet aware of what objects they should target because they don't spawn their own objects.
+There is one more important thing left to do, in order to make sure all the above Randomizers operate as expected. Since `BackgroundObjectPlacementRandomizer` spawns objects, it already knows which objects in the Scene it is dealing with; however, the rest of the Randomizers we added are not yet aware of what objects they should target because they don't spawn their own objects.
-* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Background Objects/Prefabs`.
+* **:green_circle: Action**: In the _**Project**_ tab, navigate to `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Background Objects/Prefabs`.
 * **:green_circle: Action**: Select all the files inside and from the _**Inspector**_ tab add a `TextureRandomizerTag` to them. This will add the component to all the selected files.
 * **:green_circle: Action**: Repeat the above step to add `HueOffsetRandomizerTag` and `RotationRandomizerTag` to all selected Prefabs.
 
 
 It is now time to spawn and randomize our foreground objects.
 
-* **:green_circle: Action**: Add `ForegroundObjectPlacementRandomizer` to your list of Randomizers. Click _**Add Folder**_ and select `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
+* **:green_circle: Action**: Add `ForegroundObjectPlacementRandomizer` to your list of Randomizers. Click _**Add Folder**_ and select `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`.
 * **:green_circle: Action**: Set these values for the above Randomizer: `Depth = -3, Separation Distance = 1.5, Placement Area = (5,5)`.
 
 This Randomizer uses the same algorithm as the one we used for backgrounds; however, it is defined in a separate C# class because you can only have **one of each type of Randomizer added to your Scenario**. Therefore, this is our way of differentiating between how background and foreground objects are treated.
-* **:green_circle: Action**: From the _**Project**_ tab select all the foreground Prefabs located in `Assets/Samples/Perception/0.8.0-preview.1/Tutorial Files/Foreground Objects/Phase 1/Prefabs`, and add a `RotationRandomizerTag` component to them.
+* **:green_circle: Action**: From the _**Project**_ tab select all the foreground Prefabs located in `Assets/Samples/Perception/0.9.0-preview.2/Tutorial Files/Foreground Objects/Phase 1/Prefabs`, and add a `RotationRandomizerTag` component to them.
-* **:green_circle: Action**: Drag `ForegroundObjectPlacementRandomizer` using the striped handle bar (on its left side) and drop it above `RotationRandomizer`.
+* **:green_circle: Action**: Drag `ForegroundObjectPlacementRandomizer` using the striped handlebar (on its left side) and drop it above `RotationRandomizer`.
 
 Your full list of Randomizers should now look like the screenshot below:
 
 In this folder, you will find a few types of data, depending on your `Perception Camera` settings. These can include:
 - Logs
 - JSON data
-- RGB images (raw camera output) (if the `Save Camera Output to Disk` check mark is enabled on `Perception Camera`)
+- RGB images (raw camera output) (if the `Save Camera Output to Disk` checkmark is enabled on `Perception Camera`)
-The output dataset includes a variety of information about different aspects of the active sensors in the Scene (currently only one), as well as the ground-truth generated by all active Labelers. [This page](https://github.com/Unity-Technologies/com.unity.perception/blob/master/com.unity.perception/Documentation%7E/Schema/Synthetic_Dataset_Schema.md) provides a comprehensive explanation on the schema of this dataset. We strongly recommend having a look at the page once you have completed this tutorial.
-
-> :information_source: Are the RGB images blank? This may be a bug. When using URP in OSX, having MSAA enabled on the camera may cause the output RGB images to be blank. As a workaround, you can disable MSAA and use FXAA instead, until the issue is fixed. To do this, select `Main Camera`, and in the ***Inspector*** view of the Camera component, in the ***Output*** section, set MSAA to `Off`. If you would like to use FXAA, in the ***Rendering*** section, set the Anti-aliasing option to `Fast Approximate Anti-aliasing (FXAA)`.
+The output dataset includes a variety of information about different aspects of the active sensors in the Scene (currently only one), as well as the ground-truth generated by all active Labelers. [This page](https://github.com/Unity-Technologies/com.unity.perception/blob/master/com.unity.perception/Documentation%7E/Schema/Synthetic_Dataset_Schema.md) provides a comprehensive explanation of the schema of this dataset. We strongly recommend having a look at the page once you have completed this tutorial.
 
 * **:green_circle: Action**: To get a quick feel of how the data is stored, open the folder whose name starts with `Dataset`, then open the file named `captures_000.json`. This file contains the output from `BoundingBox2DLabeler`. The `captures` array contains the position and rotation of the sensor (camera), the position and rotation of the ego (sensor group, currently only one), and the annotations made by `BoundingBox2DLabeler` for all visible objects defined in its label configuration. For each visible object, the annotations include:
 * `label_id`: The numerical id assigned to this object's label in the Labeler's label configuration
 
 * **:green_circle: Action**: Review the JSON meta-data and the images captured for the first annotated frame, and verify that the objects within them match. 
 
-### <a name="step-8">Step 8: Verify Data Using Dataset Insights</a> 
-
-
-To verify and analyze a variety of metrics for the generated data, such as number of foreground objects in each frame and degree of representation for each foreground object (label), we will now use Unity's Dataset Insights framework. This will involve running a Jupyter notebook which is conveniently packaged within a Docker file that you can download from Unity. 
+### <a name="step-8">Step 8: Explore and Visualize Your Dataset</a> 
-* **:green_circle: Action**: Download and install [Docker Desktop](https://www.docker.com/products/docker-desktop)
-* **:green_circle: Action**: Open a command line interface (Command Prompt on Windows, Terminal on Mac OS, etc.) and type the following command to run the Dataset Insights Docker image: 
-`docker run -p 8888:8888 -v <path to synthetic data>:/data -t unitytechnologies/datasetinsights:latest`, where the path to data is what we looked at earlier. You can copy the path using the _**Copy Path**_ button in the `Perception Camera` UI.
+Reviewing individual files in the dataset can be tedious and error-prone. Instead, you can use our Dataset Visualizer to efficiently visualize the captured frames along with Labeler outputs overlaid on them, as well as inspect the JSON data attached to each frame.
-> :information_source: If you get an error about the format of the command, try the command again **with quotation marks** around the folder mapping argument, i.e. `"<path to synthetic data>:/data"`.
+* **:green_circle: Action**: Open _**Window**_ -> _**Dataset Visualizer**_ -> _**Open**_. (This is also accessible from the `Perception Camera`)
-This will download a Docker image from Unity. If you get an error regarding the path to your dataset, make sure you have not included the enclosing `<` and `>` in the path and that the spaces are properly escaped.
+This will first install the visualizer and then run it. The visualizer is a Python-based tool that runs in the browser. It may take a few minutes to install the tool for the first time. Once it is installed, a browser window will open to the address `http://localhost:8501/`. 
-* **:green_circle: Action**: The image is now running on your computer. Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook:
+In certain circumstances, the tool may run but the browser window may not open. To alleviate this, once the tool is running you will get a prompt in Unity Editor, asking whether the browser window opened successfully. If it did not, you can force it to open by clicking **Manually Open**.
-<img src="Images/jupyter1.png" width="800"/>
+<img src="Images/did_visualizer_open.png" width = "300"/>
-* **:green_circle: Action**: To make sure your data is properly mounted, navigate to the `data` folder. If you see the dataset's folders there, we are good to go.
-* **:green_circle: Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
-* **:green_circle: Action**: Once in the notebook, remove the `/<GUID>` part of the `data_root = /data/<GUID>` path. Since the dataset root is already mapped to `/data`, you can use this path directly.
+> :information_source: The visualizer is a standalone tool that may be updated outside of the normal release cycles of the Perception package. You can use _**Window**_ -> _**Dataset Visualizer**_ -> _**Check For Updates**_ to check for and install updates.
-<p align="center">
-<img src="Images/jupyter2.png" width="800"/>
-</p>
+Once the browser window is open, the tool will automatically try to open the latest dataset generated using the Unity project from which you opened the visualizer. You can also open other datasets using the ***Open Dataset*** button on the left side of the screen.
-This notebook contains a variety of functions for generating plots, tables, and bounding box images that help you analyze your generated dataset. Certain parts of this notebook are currently not of use to us, such as the code meant for downloading data generated through Unity Simulation (coming later in this tutorial).
+The left sidebar also contains switches for enabling the visualization of each Labeler in the dataset. The image below shows a sample dataset opened in the visualizer, with the 2D bounding boxes overlay enabled.
-Each of the code blocks in this notebook can be executed by clicking on them to select them, and then clicking the _**Run**_  button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
+<p align="center">
+<img src="Images/visualizer_sample_synthdet.png" width = "800"/>
+</p>
-Below, you can see a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome. 
+* **:green_circle: Action**: Click _**Expand Frame**_ for the first image.
+In expanded mode, the image is enlarged and the JSON data attached to it are displayed, as seen in the screenshot below.
-<img src="Images/object_count_plot.png" width="600"/>
+<img src="Images/vis_expanded.png" width = "800"/>
-
-* **:green_circle: Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs.
+To further analyze your dataset and verify statistics such as the number of objects in each frame and more, you can use Unity's Dataset Insights, which is a Python package created for processing Perception datasets. [This guide](DatasetInsights.md) can get you started.
-This concludes Phase 1 of the Perception Tutorial. In the next phase, you will dive a little bit into randomization code and learn how to build your own custom Randomizer. 
+This concludes Phase 1 of the Perception Tutorial. In the next phase, we will dive a little bit into randomization code and learn how to build custom Randomizers. 
 
 **[Continue to Phase 2: Custom Randomizations](Phase2.md)**

com.unity.perception/package.json

     "com.unity.collections": "0.9.0-preview.6",
     "com.unity.nuget.newtonsoft-json": "1.1.2",
     "com.unity.render-pipelines.core": "7.1.6",
-    "com.unity.simulation.capture": "0.0.10-preview.23",
+    "com.unity.simulation.capture": "0.0.10-preview.24",
-    "com.unity.simulation.core": "0.0.10-preview.22"
+    "com.unity.simulation.core": "0.0.10-preview.25"
-  "version": "0.8.0-preview.4",
+  "version": "0.9.0-preview.2",
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com.unity.perception/Tests/Runtime/GroundTruthTests/RgbOutputTests.cs

+using System;
+using System.Collections;
+using NUnit.Framework;
+using Unity.Simulation;
+using UnityEngine;
+using UnityEngine.Perception.GroundTruth;
+using UnityEngine.Rendering;
+using UnityEngine.TestTools;
+#if URP_PRESENT
+using UnityEngine.Rendering.Universal;
+#endif
+#if HDRP_PRESENT
+using UnityEngine.Rendering.HighDefinition;
+#endif
+
+namespace GroundTruthTests.RgbOutputTests
+{
+    public abstract class RgbOutputTestBase : GroundTruthTestBase
+    {
+        internal static readonly Color32 clearPixelValue = new Color32(0, 0, 0, 0);
+        internal PerceptionCamera perceptionCamera;
+
+        [UnityTearDown]
+        public IEnumerator Teardown()
+        {
+            base.TearDown();
+            yield return new WaitForSeconds(2f);
+        }
+
+        internal IEnumerator GenerateRgbOutputAndValidateData(
+            Action<AsyncRequest<CaptureCamera.CaptureState>> validator = null
+        )
+        {
+            // Setup the readback
+            AsyncRequest<CaptureCamera.CaptureState> captureState = null;
+
+            void RgbCaptureReadback(AsyncRequest<CaptureCamera.CaptureState> request)
+            {
+                captureState = request;
+            }
+
+            perceptionCamera.RgbCaptureReadback += RgbCaptureReadback;
+
+            // Initialize camera and request a frame for readback
+            perceptionCamera.RequestCapture();
+            yield return null;
+
+            // perceptionCamera.RgbCaptureReadback -= RgbCaptureReadback;
+
+            // Preliminary check on capture
+            Assert.IsTrue(captureState.error == false, "Capture Request had an error.");
+
+            // Custom validation of the readback data
+            validator?.Invoke(captureState);
+        }
+
+        internal static int ImageToColorDistance(Color32 exemplar, byte[] inputs, int deviation)
+        {
+            var numItems = ArrayUtilities.Count(inputs);
+            var count = 0;
+            for (var i = 0; i < numItems; i += 4)
+            {
+                Color32 c;
+                c.r = inputs[i + 0];
+                c.g = inputs[i + 1];
+                c.b = inputs[i + 2];
+                c.a = inputs[i + 3];
+                var redDelta = Math.Abs(exemplar.r - c.r);
+                var greenDelta = Math.Abs(exemplar.g - c.g);
+                var blueDelta = Math.Abs(exemplar.b - c.b);
+                var alphaDelta = Math.Abs(exemplar.a - c.a);
+                if (redDelta > deviation || greenDelta > deviation || blueDelta > deviation || alphaDelta > deviation)
+                    ++count;
+            }
+
+            return count;
+        }
+    }
+
+#if URP_PRESENT
+    public class UrpRgbOutputTests : RgbOutputTestBase
+    {
+        static UniversalRenderPipelineAsset urpAsset => GraphicsSettings.currentRenderPipeline as UniversalRenderPipelineAsset;
+
+        // Initial Project Settings
+        static int s_InitialMsaaSampleCount;
+
+        [OneTimeSetUp]
+        public void OneTimeSetup()
+        {
+            s_InitialMsaaSampleCount = urpAsset.msaaSampleCount;
+        }
+        [UnityTearDown]
+        public IEnumerator UrpTeardown()
+        {
+            base.TearDown();
+
+            // Reset project settings
+            urpAsset.msaaSampleCount = s_InitialMsaaSampleCount;
+
+            DatasetCapture.ResetSimulation();
+            yield return null;
+        }
+
+        #region MSAA Test
+        static MsaaQuality[] s_MSAAVariations = {
+            MsaaQuality.Disabled,
+            MsaaQuality._2x,
+            MsaaQuality._8x
+        };
+        [UnityTest]
+        public IEnumerator RgbOutput_MsaaVariation_IsNotEmpty(
+            [ValueSource(nameof(s_MSAAVariations))]
+            MsaaQuality msaa
+        )
+        {
+            // Setup the camera and scene
+            var camera = SetupCamera((cam =>
+            {
+                cam.captureRgbImages = true;
+                cam.captureTriggerMode = CaptureTriggerMode.Manual;
+                perceptionCamera = cam;
+
+                // Change the MSAA Graphics setting
+                urpAsset.msaaSampleCount = (int)msaa;
+            }));
+            AddTestObjectForCleanup(camera);
+            AddTestObjectForCleanup(TestHelper.CreateLabeledCube());
+
+            // Validate RGB output image by checking if its empty
+            yield return GenerateRgbOutputAndValidateData(
+                validator: (captureState) =>
+                {
+                    // Check if color buffer is all zeros
+                    var colorBuffer = ArrayUtilities.Cast<byte>(captureState.data.colorBuffer as Array);
+                    var imageToColorDistance = ImageToColorDistance(clearPixelValue, colorBuffer, 0);
+                    Assert.IsFalse(imageToColorDistance == 0, $"[URP] RGB Output was empty for MSAA (${msaa.ToString()})");
+                }
+            );
+        }
+        #endregion
+    }
+#endif
+
+#if HDRP_PRESENT
+    public class HdrpRgbOutputTests : RgbOutputTestBase
+    {
+        static HDRenderPipelineAsset urpAsset => GraphicsSettings.currentRenderPipeline as HDRenderPipelineAsset;
+
+        // Initial Project Settings
+
+        [OneTimeSetUp]
+        public void OneTimeSetup()
+        {
+
+        }
+        [UnityTearDown]
+        public IEnumerator UrpTeardown()
+        {
+            base.TearDown();
+
+            // Reset project settings
+
+            DatasetCapture.ResetSimulation();
+            yield return null;
+        }
+
+        #region Blank Image Test
+
+        [UnityTest]
+        public IEnumerator RgbOutput_DefaultProjectSettings_IsNotEmpty()
+        {
+            // Setup the camera and scene
+            var camera = SetupCamera((cam =>
+            {
+                cam.captureRgbImages = true;
+                cam.captureTriggerMode = CaptureTriggerMode.Manual;
+                perceptionCamera = cam;
+            }));
+            AddTestObjectForCleanup(camera);
+            AddTestObjectForCleanup(TestHelper.CreateLabeledCube());
+
+            // Validate RGB output image by checking if its empty
+            yield return GenerateRgbOutputAndValidateData(
+                validator: (captureState) =>
+                {
+                    // Check if color buffer is all zeros
+                    var colorBuffer = ArrayUtilities.Cast<byte>(captureState.data.colorBuffer as Array);
+                    var imageToColorDistance = ImageToColorDistance(clearPixelValue, colorBuffer, 0);
+                    Assert.IsFalse(imageToColorDistance == 0, $"[HDRP] RGB Output was empty for default project settings.");
+                }
+            );
+        }
+        #endregion
+    }
+#endif
+
+}

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com.unity.perception/Tests/Runtime/Randomization/ScenarioTests/AllMembersAndParametersTestRandomizer.cs

+using UnityEngine.Perception.Randomization.Parameters;
+using UnityEngine.Perception.Randomization.Randomizers;
+using UnityEngine.Perception.Randomization.Samplers;
+
+namespace UnityEngine.Perception.RandomizationTests.ScenarioTests
+{
+    public class AllMembersAndParametersTestRandomizer: Randomizer
+    {
+        // Members
+        public bool booleanMember = false;
+        public int intMember = 4;
+        public uint uintMember = 2;
+        public float floatMember = 5;
+        public Vector2 vector2Member = new Vector2(4, 7);
+        public UniformSampler unsupportedMember = new UniformSampler();
+
+        // Parameters
+        public BooleanParameter booleanParam = new BooleanParameter()
+        {
+            value = new ConstantSampler(1)
+        };
+        public FloatParameter floatParam = new FloatParameter()
+        {
+            value = new AnimationCurveSampler()
+        };
+        public IntegerParameter integerParam = new IntegerParameter()
+        {
+            value = new UniformSampler(-3, 7)
+        };
+        public Vector2Parameter vector2Param = new Vector2Parameter()
+        {
+            x = new ConstantSampler(2),
+            y = new UniformSampler(-4, 8)
+        };
+        public Vector3Parameter vector3Param = new Vector3Parameter()
+        {
+            x = new NormalSampler(-5, 9, 4, 2),
+            y = new ConstantSampler(3),
+            z = new AnimationCurveSampler()
+        };
+        public Vector4Parameter vector4Param = new Vector4Parameter()
+        {
+            x = new NormalSampler(-5, 9, 4, 2),
+            y = new ConstantSampler(3),
+            z = new AnimationCurveSampler(),
+            w = new UniformSampler(-12, 42)
+        };
+        public ColorRgbCategoricalParameter colorRgbCategoricalParam = new ColorRgbCategoricalParameter();
+    };
+}

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com.unity.perception/Runtime/PerceptionAnalytics.cs

+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Unity.Simulation;
+using UnityEngine.Analytics;
+using UnityEngine;
+using UnityEngine.Perception.GroundTruth;
+using UnityEngine.Perception.Randomization.Randomizers;
+using UnityEngine.Perception.Randomization.Scenarios;
+#if UNITY_EDITOR
+using UnityEditor;
+#endif
+
+namespace UnityEngine.Perception.Analytics
+{
+
+    /// <summary>
+    /// Editor and Runtime analytics for the Perception package.
+    /// </summary>
+    /// <remarks>
+    /// To add an event:
+    /// 1. Create a constant with the name of the event (eg: <see cref="k_EventScenarioInformation"/>)
+    /// 2. Add the constant to <see cref="allEvents" />
+    /// 3. Create a function that will report data for the event and at the start of it call
+    /// <see cref="TryRegisterPerceptionAnalyticsEvent" /> with the event name defined in step 1.
+    /// Note: Remember to use the conditional "#if UNITY_EDITOR" if adding editor analytics.
+    /// </remarks>
+    public static class PerceptionAnalytics
+    {
+        const string k_VendorKey = "unity.perception";
+        const int k_MaxElementsInStruct = 100;
+        const int k_MaxEventsPerHour = 100;
+
+        #region Setup
+        [RuntimeInitializeOnLoadMethod]
+        static void OnInitializeOnLoad()
+        {
+            Manager.Instance.ShutdownNotification += OnSimulationShutdown;
+        }
+
+        static void OnSimulationShutdown()
+        {
+            var perceptionCamera = Object.FindObjectOfType<PerceptionCamera>();
+            ReportScenarioInformation(
+                perceptionCamera,
+                ScenarioBase.activeScenario
+            );
+        }
+
+        #endregion
+
+        /// <summary>
+        /// Stores whether each event has been registered successfully or not.
+        /// </summary>
+        static Dictionary<AnalyticsEvent, bool> s_EventRegistrationStatus = new Dictionary<AnalyticsEvent, bool>();
+
+        #region Event Definitions
+        static readonly AnalyticsEvent k_EventScenarioInformation = new AnalyticsEvent(
+            "perceptionScenarioInformation", AnalyticsEventType.RuntimeAndEditor, 1
+        );
+        static readonly AnalyticsEvent k_EventRunInUnitySimulation = new AnalyticsEvent(
+            "runinunitysimulation", AnalyticsEventType.Editor, 1
+        );
+
+        /// <summary>
+        /// All supported events. If an event does not exist in this list, an error will be thrown during
+        /// <see cref="TryRegisterPerceptionAnalyticsEvent" />.
+        /// </summary>
+        static IEnumerable<AnalyticsEvent> allEvents => new[]
+        {
+            k_EventScenarioInformation,
+            k_EventRunInUnitySimulation
+        };
+        #endregion
+
+        #region Helpers
+
+        /// <summary>
+        /// Tries to register an event and returns whether it was registered successfully. The result is also cached in
+        /// the <see cref="s_EventRegistrationStatus" /> dictionary.
+        /// </summary>
+        /// <param name="theEvent">The name of the event.</param>
+        /// <returns>Whether the event was successfully registered/</returns>
+        static bool TryRegisterPerceptionAnalyticsEvent(AnalyticsEvent theEvent)
+        {
+            // Make sure the event exists in the dictionary
+            if (!s_EventRegistrationStatus.ContainsKey(theEvent))
+            {
+                if (allEvents.Contains(theEvent))
+                    s_EventRegistrationStatus[theEvent] = false;
+                else
+                    throw new NotSupportedException($"Unrecognized event {theEvent} not included in {nameof(allEvents)}.");
+            }
+
+            // If registered previously, return true
+            if (s_EventRegistrationStatus[theEvent])
+                return true;
+
+            // Try registering the event and update the dictionary accordingly
+            s_EventRegistrationStatus[theEvent] = true;
+#if UNITY_EDITOR
+            var status = EditorAnalytics.RegisterEventWithLimit(theEvent.name, k_MaxEventsPerHour, k_MaxElementsInStruct, k_VendorKey);
+#else
+            var status = UnityEngine.Analytics.Analytics.RegisterEvent(theEvent.name, k_MaxEventsPerHour, k_MaxElementsInStruct, k_VendorKey);
+#endif
+            s_EventRegistrationStatus[theEvent] &= status == AnalyticsResult.Ok;
+
+            return s_EventRegistrationStatus[theEvent];
+        }
+
+        /// <summary>
+        /// Based on the value of type for <see cref="theEvent" />, sends an Editor Analytics event,
+        /// a Runtime Analytics event, or both.
+        /// </summary>
+        /// <param name="theEvent">The analytics event.</param>
+        /// <param name="data">Payload of the event.</param>
+        static void SendPerceptionAnalyticsEvent(AnalyticsEvent theEvent, object data)
+        {
+#if UNITY_EDITOR
+            if (theEvent.type == AnalyticsEventType.Editor || theEvent.type == AnalyticsEventType.RuntimeAndEditor)
+            {
+                EditorAnalytics.SendEventWithLimit(theEvent.name, data, theEvent.versionId);
+            }
+#else
+            if (theEvent.type == AnalyticsEventType.Runtime || theEvent.type == AnalyticsEventType.RuntimeAndEditor)
+            {
+                UnityEngine.Analytics.Analytics.SendEvent(theEvent.name, data, theEvent.versionId, theEvent.prefix);
+            }
+#endif
+        }
+
+        #endregion
+
+        #region Event: Scenario Information
+
+        /// <summary>
+        /// Which labelers will be identified and included in the analytics information.
+        /// </summary>
+        public static readonly string[] labelerAllowList = new[]
+        {
+            "BoundingBox3DLabeler", "BoundingBox2DLabeler", "InstanceSegmentationLabeler",
+            "KeypointLabeler", "ObjectCountLabeler", "SemanticSegmentationLabeler", "RenderedObjectInfoLabeler"
+        };
+
+        static void ReportScenarioInformation(
+            PerceptionCamera cam,
+            ScenarioBase scenario
+        )
+        {
+            if (!TryRegisterPerceptionAnalyticsEvent(k_EventScenarioInformation))
+                return;
+
+            var randomizers = scenario ? scenario.activeRandomizers : new List<Randomizer>();
+            var data = ScenarioCompletedData.FromCameraAndRandomizers(cam, randomizers);
+            SendPerceptionAnalyticsEvent(k_EventScenarioInformation, data);
+        }
+
+        #endregion
+
+        #region Event: Run In Unity Simulation
+        public static void ReportRunInUnitySimulationStarted(Guid runId, int totalIterations, int instanceCount, string existingBuildId)
+        {
+            if (!TryRegisterPerceptionAnalyticsEvent(k_EventRunInUnitySimulation))
+                return;
+
+            var data = new RunInUnitySimulationData
+            {
+                runId = runId.ToString(),
+                totalIterations = totalIterations,
+                instanceCount = instanceCount,
+                existingBuildId = existingBuildId,
+                runStatus = RunStatus.Started.ToString()
+            };
+
+            SendPerceptionAnalyticsEvent(k_EventRunInUnitySimulation, data);
+        }
+
+        public static void ReportRunInUnitySimulationFailed(Guid runId, string errorMessage)
+        {
+            if (!TryRegisterPerceptionAnalyticsEvent(k_EventRunInUnitySimulation))
+                return;
+
+            var data = new RunInUnitySimulationData
+            {
+                runId = runId.ToString(),
+                errorMessage = errorMessage,
+                runStatus = RunStatus.Failed.ToString()
+            };
+
+            SendPerceptionAnalyticsEvent(k_EventRunInUnitySimulation, data);
+        }
+
+        public static void ReportRunInUnitySimulationSucceeded(Guid runId, string runExecutionId)
+        {
+            if (!TryRegisterPerceptionAnalyticsEvent(k_EventRunInUnitySimulation))
+                return;
+
+            var data = new RunInUnitySimulationData
+            {
+                runId = runId.ToString(),
+                runExecutionId = runExecutionId,
+                runStatus = RunStatus.Succeeded.ToString()
+            };
+
+            SendPerceptionAnalyticsEvent(k_EventRunInUnitySimulation, data);
+        }
+
+        #endregion
+    }
+}

com.unity.perception/Runtime/PerceptionAnalytics.cs.meta

+fileFormatVersion: 2
+guid: 38657354e82b4c9b8370c9c55a39b1e3
+timeCreated: 1629939608

com.unity.perception/Runtime/PerceptionAnalyticsStructs.cs

+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Reflection;
+using Unity.Simulation;
+using UnityEngine.Assertions;
+using UnityEngine.Perception.GroundTruth;
+using UnityEngine.Perception.Randomization.Parameters;
+using UnityEngine.Perception.Randomization.Randomizers;
+using UnityEngine.Perception.Randomization.Samplers;
+
+namespace UnityEngine.Perception.Analytics
+{
+    #region Common
+
+    enum AnalyticsEventType
+    {
+        Runtime,
+        Editor,
+        RuntimeAndEditor
+    }
+    struct AnalyticsEvent
+    {
+        public string name { get; private set; }
+        public AnalyticsEventType type { get; private set; }
+        public int versionId { get; private set; }
+        public string prefix { get; private set; }
+
+        public AnalyticsEvent(string name, AnalyticsEventType type, int versionId, string prefix = "")
+        {
+            this.name = name;
+            this.type = type;
+            this.versionId = versionId;
+            this.prefix = prefix;
+        }
+    }
+
+    #endregion
+
+    #region Run In Unity Simulation
+
+    [Serializable]
+    enum RunStatus
+    {
+        Started,
+        Failed,
+        Succeeded
+    }
+
+    [Serializable]
+    struct RunInUnitySimulationData
+    {
+        public string runId;
+        public int totalIterations;
+        public int instanceCount;
+        public string existingBuildId;
+        public string errorMessage;
+        public string runExecutionId;
+        public string runStatus;
+    }
+
+    #endregion
+
+    #region Scenario Information
+
+    [Serializable]
+    public class PerceptionCameraData
+    {
+        public string captureTriggerMode;
+        // same as "firstCaptureFrame" of the Perception Camera
+        public int startAtFrame;
+        public int framesBetweenCaptures;
+    }
+
+    [Serializable]
+    public class LabelerData
+    {
+        public string name;
+        public int labelConfigCount;
+        public string objectFilter = "";
+        public int animationPoseCount;
+
+        public static LabelerData FromLabeler(CameraLabeler labeler)
+        {
+            var labelerType = labeler.GetType();
+            var labelerName = labelerType.Name;
+            if (!PerceptionAnalytics.labelerAllowList.Contains(labelerName))
+                return null;
+
+            var labelerData = new LabelerData()
+            {
+                name = labelerName
+            };
+
+            switch (labeler)
+            {
+                case BoundingBox3DLabeler bb3dl:
+                    labelerData.labelConfigCount = bb3dl.idLabelConfig.labelEntries.Count;
+                    break;
+                case BoundingBox2DLabeler bb2dl:
+                    labelerData.labelConfigCount = bb2dl.idLabelConfig.labelEntries.Count;
+                    break;
+                case InstanceSegmentationLabeler isl:
+                    labelerData.labelConfigCount = isl.idLabelConfig.labelEntries.Count;
+                    break;
+                case KeypointLabeler kpl:
+                    labelerData.labelConfigCount = kpl.idLabelConfig.labelEntries.Count;
+                    labelerData.objectFilter = kpl.objectFilter.ToString();
+                    labelerData.animationPoseCount = kpl.animationPoseConfigs.Count;
+                    break;
+                case ObjectCountLabeler ocl:
+                    labelerData.labelConfigCount = ocl.labelConfig.labelEntries.Count;
+                    break;
+                case SemanticSegmentationLabeler ssl:
+                    labelerData.labelConfigCount = ssl.labelConfig.labelEntries.Count;
+                    break;
+                case RenderedObjectInfoLabeler rol:
+                    labelerData.labelConfigCount = rol.idLabelConfig.labelEntries.Count;
+                    break;
+                default:
+                    labelerData = null;
+                    break;
+            }
+
+            return labelerData;
+        }
+    }
+
+    [Serializable]
+    public class MemberData
+    {
+        public string name;
+        public string value;
+        public string type;
+    }
+
+    [Serializable]
+    public class ParameterField
+    {
+        public string name;
+        public string distribution;
+        public float value;
+        public float rangeMinimum;
+        public float rangeMaximum;
+        public float mean;
+        public float stdDev;
+        public int categoricalParameterCount;
+
+        public static ParameterField ExtractSamplerInformation(ISampler sampler, string fieldName = "value")
+        {
+            switch (sampler)
+            {
+                case AnimationCurveSampler _:
+                    return new ParameterField()
+                    {
+                        name = fieldName,
+                        distribution = "AnimationCurve"
+                    };
+                case ConstantSampler cs:
+                    return new ParameterField()
+                    {
+                        name = fieldName,
+                        distribution = "Constant",
+                        value = cs.value
+                    };
+                case NormalSampler ns:
+                    return new ParameterField()
+                    {
+                        name = fieldName,
+                        distribution = "Normal",
+                        mean = ns.mean,
+                        stdDev = ns.standardDeviation,
+                        rangeMinimum = ns.range.minimum,
+                        rangeMaximum = ns.range.maximum
+                    };
+                case UniformSampler us:
+                    return new ParameterField()
+                    {
+                        name = fieldName,
+                        distribution = "Uniform",
+                        rangeMinimum = us.range.minimum,
+                        rangeMaximum = us.range.maximum
+                    };
+                default:
+                    return null;
+            }
+        }
+
+        public static List<ParameterField> ExtractSamplersInformation(IEnumerable<ISampler> samplers, IEnumerable<string> indexToNameMapping)
+        {
+            var samplersList = samplers.ToList();
+            var indexToNameMappingList = indexToNameMapping.ToList();
+            if (samplersList.Count > indexToNameMappingList.Count)
+                throw new Exception("Insufficient names provided for mapping ParameterFields");
+            return samplersList.Select((t, i) => ExtractSamplerInformation(t, indexToNameMappingList[i])).ToList();
+        }
+    }
+
+    [Serializable]
+    public class ParameterData
+    {
+        public string name;
+        public string type;
+        public List<ParameterField> fields;
+    }
+
+    [Serializable]
+    public class RandomizerData
+    {
+        public string name;
+        public MemberData[] members;
+        public ParameterData[] parameters;
+
+        public static RandomizerData FromRandomizer(Randomizer randomizer)
+        {
+            if (randomizer == null)
+                return null;
+
+            var data = new RandomizerData()
+            {
+                name = randomizer.GetType().Name,
+            };
+
+            var randomizerType = randomizer.GetType();
+
+            // Filter out randomizers which could be considered personally identifiable.
+            if (randomizerType.Namespace == null || !randomizerType.Namespace.StartsWith("UnityEngine.Perception"))
+                return null;
+
+            // Naming configuration
+            var vectorFieldNames = new[] { "x", "y", "z", "w" };
+            var hsvaFieldNames = new[] { "hue", "saturation", "value", "alpha" };
+            var rgbFieldNames = new[] { "red", "green", "blue" };
+
+            // Add member fields and parameters separately
+            var members = new List<MemberData>();
+            var parameters = new List<ParameterData>();
+            foreach (var field in randomizerType.GetFields(BindingFlags.Public | BindingFlags.Instance))
+            {
+                var member = field.GetValue(randomizer);
+                var memberType = member.GetType();
+
+                // If member is either a categorical or numeric parameter
+                if (memberType.IsSubclassOf(typeof(Parameter)))
+                {
+                    var pd = new ParameterData()
+                    {
+                        name = field.Name,
+                        type = memberType.Name,
+                        fields = new List<ParameterField>()
+                    };
+
+                    // All included parameter types
+                    switch (member)
+                    {
+                        case CategoricalParameterBase cp:
+                            pd.fields.Add(new ParameterField()
+                            {
+                                name = "values",
+                                distribution = "Categorical",
+                                categoricalParameterCount = cp.probabilities.Count
+                            });
+                            break;
+                        case BooleanParameter bP:
+                            pd.fields.Add(ParameterField.ExtractSamplerInformation(bP.value));
+                            break;
+                        case FloatParameter fP:
+                            pd.fields.Add(ParameterField.ExtractSamplerInformation(fP.value));
+                            break;
+                        case IntegerParameter iP:
+                            pd.fields.Add(ParameterField.ExtractSamplerInformation(iP.value));
+                            break;
+                        case Vector2Parameter v2P:
+                            pd.fields.AddRange(ParameterField.ExtractSamplersInformation(v2P.samplers, vectorFieldNames));
+                            break;
+                        case Vector3Parameter v3P:
+                            pd.fields.AddRange(ParameterField.ExtractSamplersInformation(v3P.samplers, vectorFieldNames));
+                            break;
+                        case Vector4Parameter v4P:
+                            pd.fields.AddRange(ParameterField.ExtractSamplersInformation(v4P.samplers, vectorFieldNames));
+                            break;
+                        case ColorHsvaParameter hsvaP:
+                            pd.fields.AddRange(ParameterField.ExtractSamplersInformation(hsvaP.samplers, hsvaFieldNames));
+                            break;
+                        case ColorRgbParameter rgbP:
+                            pd.fields.AddRange(ParameterField.ExtractSamplersInformation(rgbP.samplers, rgbFieldNames));
+                            break;
+                    }
+
+                    parameters.Add(pd);
+                }
+                else
+                {
+                    members.Add(new MemberData()
+                    {
+                        name = field.Name,
+                        type = memberType.FullName,
+                        value = member.ToString()
+                    });
+                }
+            }
+
+            data.members = members.ToArray();
+            data.parameters = parameters.ToArray();
+            return data;
+        }
+    }
+
+    [Serializable]
+    public class ScenarioCompletedData
+    {
+        public string platform;
+        public PerceptionCameraData perceptionCamera;
+        public LabelerData[] labelers;
+        public RandomizerData[] randomizers;
+
+        internal static ScenarioCompletedData FromCameraAndRandomizers(
+            PerceptionCamera cam,
+            IEnumerable<Randomizer> randomizers
+        )
+        {
+            var data = new ScenarioCompletedData()
+            {
+                platform = (Configuration.Instance.IsSimulationRunningInCloud()) ? "USim": Application.platform.ToString()
+            };
+
+            if (cam != null)
+            {
+                // Perception Camera Data
+                data.perceptionCamera = new PerceptionCameraData()
+                {
+                    captureTriggerMode = cam.captureTriggerMode.ToString(),
+                    startAtFrame = cam.firstCaptureFrame,
+                    framesBetweenCaptures = cam.framesBetweenCaptures
+                };
+
+                // Labeler Data
+                data.labelers = cam.labelers
+                    .Select(LabelerData.FromLabeler)
+                    .Where(labeler => labeler != null).ToArray();
+            }
+
+            var randomizerList = randomizers.ToArray();
+            if (randomizerList.Any())
+            {
+                data.randomizers = randomizerList
+                    .Select(RandomizerData.FromRandomizer)
+                    .Where(x => x != null).ToArray();
+            }
+            else
+            {
+                data.randomizers = new RandomizerData[] { };
+            }
+
+            return data;
+        }
+    }
+
+    #endregion
+}

com.unity.perception/Runtime/PerceptionAnalyticsStructs.cs.meta

+fileFormatVersion: 2
+guid: 78500bfc6066482ca6af77c3586dcbd2
+timeCreated: 1632680191

com.unity.perception/Documentation~/Tutorial/DatasetInsights.md

+# Verifying and Analyzing Perception Datasets with Dataset Insights
+
+Unity's [Datasets Insights](https://github.com/Unity-Technologies/datasetinsights) is a Python package that provides a variety of tools for downloading, processing, and analyzing datasets generated using the Perception package. In addition to a Python library, the package comes with a sample Jupyter notebook that helps you load datasets and verify some of their most commonly needed statistics.
+
+In this guide, we will go through the steps involved in opening Perception datasets and verifying them using the provided Jupyter notebook. This includes both datasets that are generated locally and those generated with Unity Simulation. To learn how to generate datasets locally follow [Phase 1](Tutorial/../Phase1.md) of the Perception Tutorial. For the Unity Simulation workflow, follow [Phase 3](Tutorial/../Phase3.md) of the tutorial.
+
+* **:green_circle: Action**: Download and install [Docker Desktop](https://www.docker.com/products/docker-desktop)
+
+## Locally generated datasets
+
+* **:green_circle: Action**: Open a command line interface (Command Prompt on Windows, Terminal on Mac OS, etc.) and type the following command to run the Dataset Insights Docker image: 
+`docker run -p 8888:8888 -v <path to synthetic data>:/data -t unitytechnologies/datasetinsights:latest`, where the path to data is what we looked at earlier. You can copy the path using the _**Copy Path**_ button in the `Perception Camera` UI.
+
+> :information_source: If you get an error about the format of the command, try the command again **with quotation marks** around the folder mapping argument, i.e. `"<path to synthetic data>:/data"`.
+
+This will download a Docker image from Unity. If you get an error regarding the path to your dataset, make sure you have not included the enclosing `<` and `>` in the path and that the spaces are properly escaped.
+
+* **:green_circle: Action**: The image is now running on your computer. Open a web browser and navigate to `http://localhost:8888` to open the Jupyter notebook:
+
+<p align="center">
+<img src="Images/jupyter1.png" width="800"/>
+</p>
+
+* **:green_circle: Action**: To make sure your data is properly mounted, navigate to the `data` folder. If you see the dataset's folders there, we are good to go.
+* **:green_circle: Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
+* **:green_circle: Action**: Once in the notebook, remove the `/<GUID>` part of the `data_root = /data/<GUID>` path. Since the dataset root is already mapped to `/data`, you can use this path directly.
+
+<p align="center">
+<img src="Images/jupyter2.png" width="800"/>
+</p>
+
+This notebook contains a variety of functions for generating plots, tables, and bounding box images that help you analyze your generated dataset. Certain parts of this notebook are currently not of use to us, such as the code meant for downloading data generated through Unity Simulation (coming later in this tutorial).
+
+Each of the code blocks in this notebook can be executed by clicking on them to select them, and then clicking the _**Run**_  button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
+
+Below, you can see a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome. 
+
+
+<p align="center">
+<img src="Images/object_count_plot.png" width="600"/>
+</p>
+
+
+* **:green_circle: Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs.
+
+
+## Datasets generated with Unity Simulation
+
+For these datasets we recommend using a slightly different command to open the notebook, as we do not need to mount a specific dataset folder. Instead, we mount a folder which will hold our downloaded datasets.
+
+* **:green_circle: Action**: Open the notebook using the command below:
+ 
+ `docker run -p 8888:8888 -v <download path>/data:/data -t unitytechnologies/datasetinsights:latest`
+
+In the above command, replace `<download path>` with the location on your computer in which you wish to download your data.
+
+Once the Docker image is running, the rest of the workflow is quite similar to what we did for locally generated data. The only difference is that we need to uncomment certain lines of code from the notebook to download the dataset.
+
+* **:green_circle: Action**: Open a web browser and navigate to `http://localhost:8888` to open the notebook.
+* **:green_circle: Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
+* **:green_circle: Action**: In the `data_root = /data/<GUID>` line, the `<GUID>` part will be the location inside your `<download path>` where the data will be downloaded. Therefore, you can just remove it so as to have data downloaded directly to the path you previously specified:
+
+<p align="center">
+<img src="Images/di_usim_1.png" width="900"/>
+</p>
+
+* **:green_circle: Action**: In the block of code titled "Unity Simulation [Optional]", uncomment the lines that assign values to variables, and insert the correct values, based on information from your Unity Simulation run. 
+
+We have previously learned how to obtain the `run_execution_id` and `project_id`. You can remove the value already present for `annotation_definition_id` and leave it blank. What's left is the `access_token`.
+
+* **:green_circle: Action**: Return to your command-line interface and run the `usim inspect auth` command.
+
+MacOS:
+ `USimCLI/mac/usim inspect auth`
+
+If you receive errors regarding authentication, your token might have timed out. Repeat the login step (`usim login auth`) to login again and fix this issue.
+
+A sample output from `usim inspect auth` will look like below:
+
+```
+Protect your credentials. They may be used to impersonate your requests.
+access token: Bearer 0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp
+expires in: 2:00:05.236227
+expired: False
+refresh token: FW4c3YRD4IXi6qQHv3Y9W-rwg59K7k0Te9myKe7Zo6M003f.k4Dqo0tuoBdf-ncm003fX2RAHQ
+updated: 2020-10-02 14:50:11.412979
+```
+
+The `access_token` you need for your Dataset Insights notebook is the access token shown by the above command, minus the `'Bearer '` part. So, in this case, we should copy `0CfQbhJ6gjYIHjC6BaP5gkYn1x5xtAp7ZA9I003fTNT1sFp` into the notebook. 
+
+* **:green_circle: Action**: Copy the access token excluding the `'Bearer '` part to the corresponding field in the notebook.
+
+Once you have entered all the information, the block of code should look like the screenshot below (the actual values you input will be different):
+
+<p align="center">
+<img src="Images/di_usim_2.png" width="800"/>
+</p>
+
+
+* **:green_circle: Action**: Continue to the next code block and run it to download all the metadata files from the generated dataset. This includes JSON files and logs but does not include images (which will be downloaded later). 
+
+You will see a progress bar while the data downloads:
+
+<p align="center">
+<img src="Images/di_usim_3.png" width="800"/>
+</p>
+
+
+The next couple of code blocks (under "Load dataset metadata") analyze the downloaded metadata and display a table containing annotation-definition-ids for the various metrics defined in the dataset.
+
+* **:green_circle: Action**: Once you reach the code block titled "Built-in Statistics", make sure the value assigned to the field `rendered_object_info_definition_id` matches the id displayed for this metric in the table output by the code block immediately before it. The screenshot below demonstrates this (note that your ids might differ from the ones here):
+
+<p align="center">
+<img src="Images/di_usim_4.png" width="800"/>
+</p>
+
+Follow the rest of the steps inside the notebook to generate a variety of plots and stats. 
+
+Keep in mind that this notebook is provided just as an example, and you can modify and extend it according to your own needs using the tools provided by the [Dataset Insights framework](https://datasetinsights.readthedocs.io/en/latest/).

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