Re-adding index.md and adding script for creating it based on README.md

.yamato/upm-ci-full.yml

     - .yamato/upm-ci-testprojects.yml#{{project.name}}_linux_editmode_{{editor.version}}
     {% endfor %}
     {% endfor %}
+    - .yamato/upm-ci-full.yml#check_index_md
 
 all_tests:
   name: Complete Tests
     - .yamato/upm-ci-performance.yml#{{project.name}}_linux_standalone_{{editor.version}}
     {% endfor %}
     {% endfor %}
+    - .yamato/upm-ci-full.yml#check_index_md
 
 all_tests_nightly_perf:
   name: Nightly Performance Tests
     - .yamato/upm-ci-performance.yml#{{project.name}}_linux_standalone_{{editor.version}}
     {% endfor %}
     {% endfor %}
+
+check_index_md:
+  name: Check Index.md
+  agent:
+    type: Unity::VM
+    image: cds-ops/ubuntu-18.04-agent:stable
+    flavor: b1.medium
+  interpreter: python
+  commands:
+    - cd com.unity.perception/Documentation~
+    - python UpdateIndexMd.py -check

README.md

 
 <img src="com.unity.perception/Documentation~/images/banner2.PNG" align="middle"/>
 
+[//]: # (Exclude from Index.md)
+
+
+[//]: # (End Exclude)
 
 [![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE.md)
 
 
 The [Unity Simulation Smart Camera Example](https://github.com/Unity-Technologies/Unity-Simulation-Smart-Camera-Outdoor) illustrates how the Perception package could be used in a smart city or autonomous vehicle simulation. You can generate datasets locally or at scale in [Unity Simulation](https://unity.com/products/unity-simulation).
 
+[//]: # (Exclude from Index.md)
+
 ## Local development
 The repository includes two projects for local development in `TestProjects` folder, one set up for HDRP and the other for URP.
 
+
+[//]: # (End Exclude)
 
 ## Known issues
 

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

+<img src="images/unity-wide.png" align="middle" width="3000"/>
+
+<img src="images/banner2.PNG" align="middle"/>
+
+
+
+[![license badge](https://img.shields.io/badge/license-Apache--2.0-green.svg)](LICENSE.md)
+
+> com.unity.perception is in active development. Its features and API are subject to significant change as development progresses.
+
+BAD CONTENT
+
+# Perception Package ([Unity Computer Vision](https://unity.com/computer-vision))
+
+The Perception package provides a toolkit for generating large-scale datasets for computer vision training and validation. It is focused on a handful of camera-based use cases for now and will ultimately expand to other forms of sensors and machine learning tasks.
+
+Visit the [Unity Computer Vision](https://unity.com/computer-vision) page for more information on our tools and offerings!
+
+## Getting Started
+
+**[Quick Installation Instructions](SetupSteps.md)**  
+Get your local Perception workspace up and running quickly. Recommended for users with prior Unity experience.
+
+**[Perception Tutorial](Tutorial/TUTORIAL.md)**  
+Detailed instructions covering all the important steps from installing Unity Editor, to creating your first computer vision data generation project, building a randomized Scene, and generating large-scale synthetic datasets by leveraging the power of Unity Simulation.  No prior Unity experience required.
+
+**[Human Pose Labeling and Randomization Tutorial](HPTutorial/TUTORIAL.md)**  
+Step by step instructions for using the keypoint, pose, and animation randomization tools included in the Perception package. It is recommended that you finish Phase 1 of the Perception Tutorial above before starting this tutorial.
+
+## Documentation
+In-depth documentation on individual components of the package. 
+
+|Feature|Description|
+|---|---|
+|[Labeling](GroundTruthLabeling.md)|A component that marks a GameObject and its descendants with a set of labels|
+|[Label Config](GroundTruthLabeling.md#label-config)|An asset that defines a taxonomy of labels for ground truth generation|
+|[Perception Camera](PerceptionCamera.md)|Captures RGB images and ground truth from a [Camera](https://docs.unity3d.com/Manual/class-Camera.html).|
+|[Dataset Capture](DatasetCapture.md)|Ensures sensors are triggered at proper rates and accepts data for the JSON dataset.|
+|[Randomization](Randomization/Index.md)|The Randomization tool set lets you integrate domain randomization principles into your simulation.|
+
+## Example Projects
+
+### SynthDet
+
+<img src="images/synthdet.png"/>
+
+[SynthDet](https://github.com/Unity-Technologies/SynthDet) is an end-to-end solution for training a 2D object detection model using synthetic data.
+
+### Unity Simulation Smart Camera example
+<img src="images/smartcamera.png"/>
+
+The [Unity Simulation Smart Camera Example](https://github.com/Unity-Technologies/Unity-Simulation-Smart-Camera-Outdoor) illustrates how the Perception package could be used in a smart city or autonomous vehicle simulation. You can generate datasets locally or at scale in [Unity Simulation](https://unity.com/products/unity-simulation).
+
+
+
+## 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
+
+## License
+* [License](com.unity.perception/LICENSE.md)
+
+## Support 
+
+For general questions or concerns please contact the Computer Vision team at computer-vision@unity3d.com.
+
+For feedback, bugs, or other issues please file a GitHub issue and the Computer Vision team will investigate the issue as soon as possible.
+
+## Citation
+If you find this package useful, consider citing it using:
+```
+@misc{com.unity.perception2021,
+    title={Unity {P}erception Package},
+    author={{Unity Technologies}},
+    howpublished={\url{https://github.com/Unity-Technologies/com.unity.perception}},
+    year={2020}
+}
+```

com.unity.perception/Documentation~/UpdateIndexMd.py

+# This script has two functions:
+# Without the -check parameter it updates com.unity.perception/Documentation~/Index.md with the content in README.md
+# With the -check parameter it checks com.unity.perception/Documentation~/Index.md to make sure it is up to date with README.md
+import argparse
+import re
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-check", help="Check that Index.md is up to date with README.md", action='store_true')
+
+args = parser.parse_args()
+
+with open("../../README.md", "r") as f:
+    content = f.read()
+
+content_updated = content.replace("com.unity.perception/Documentation~/", "")
+
+def excluderepl(matchobj):
+    if matchobj.group(0).count(r'[//]: # (End Exclude)') > 1:
+        return matchobj.group(0)
+    else:
+        return ''
+
+content_updated = re.sub('\[\/\/\]\: \# \(Exclude from Index\.md\).*?\[\/\/\]\: \# \(End Exclude\)', excluderepl, content_updated, flags=re.DOTALL)
+
+if args.check:
+    with open("Index.md", "r") as indexFile:
+        indexContent = indexFile.read()
+
+    if indexContent == content_updated:
+        exit(0)
+    else:
+        print("com.unity.perception/Documentation~/Index.md is not up to date. Run com.unity.perception/Documentation~/UpdateIndexMd.py to update it.")
+        exit(1)
+else:
+    with open("Index.md", "w") as indexFile:
+        indexFile.write(content_updated)