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Unity Perception Package (com.unity.perception)
The Perception package provides a toolkit for generating large-scale datasets for perception-based machine learning, training and validation. It is focused on capturing ground truth for Camera-based use cases. In the future, the Perception package will include other types of sensors and machine learning tasks.
Visit the pages below for in-depth documentation on inidividual components of the package.
| Feature | Description |
|---|---|
| Labeling | A component that marks a GameObject and its descendants with a set of labels |
| LabelConfig | An asset that defines a taxonomy of labels for ground truth generation |
| Perception Camera | Captures RGB images and ground truth from a Camera. |
| DatasetCapture | Ensures sensors are triggered at proper rates and accepts data for the JSON dataset. |
| Randomization (Experimental) | The Randomization tool set lets you integrate domain randomization principles into your simulation. |
Getting Started
Quick Installation Instructions
Get your local Perception workspace up and running quickly. Recommended for users with prior Unity experience.
Perception Tutorial
Detailed instructions covering all the important steps from installing Unity Editor, to creating your first Perception project, building a randomized Scene, and generating large-scale synthetic datasets by leveraging the power of Unity Simulation. No prior Unity experience required.
Preview package
This package is available as a preview, so it is not ready for production use. The features and documentation in this package might change before it is verified for release.
Example projects using Perception
SynthDet
SynthDet is an end-to-end solution for training a 2D object detection model using synthetic data.
Unity Simulation Smart Camera example
The Unity Simulation Smart Camera Example illustrates how Perception could be used in a smart city or autonomous vehicle simulation. You can generate datasets locally or at scale in 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