Aria Synthetic Environments Dataset
A large-scale, fully simulated dataset of procedurally-generated interior scenes
Aria Synthetic Environments is a synthetic dataset, created from procedurally-generated interior layouts filled with 3D objects, simulated with the sensor characteristics of Aria glasses.
Unlike prior datasets for 3D scene understanding, which are typically not large enough for ML training, the Aria Synthetic Environments Dataset sets a new precedent for the scale of indoor environment datasets. This surfaces exciting new research opportunities for tasks related to 3D scene reconstruction, and object detection and tracking.
Simulated sensor data per sequence
Ground Truth Annotations
100,000 unique scenes, procedurally generated
By creating a fully-simulated dataset, researchers can explore new methods for scene understanding tasks.
100,000 unique multi-room interior scenes
The Aria Synthetic Environments Dataset has been procedurally generated to produce a diverse set of interior scenes. Each scene has a unique room graph connecting multiple rooms, and unique placement of architectural features, such as windows, doors, and pillars.
Populated with high-quality 3D objects
Each of the 100,000 unique scenes, is filled with objects from a digital library, each with high-quality materials and geometry. Objects are diverse and placed according to a simple set of rules that result in a physically-valid location for each object.
Rendered with precisely simulated sensor characteristics
Each simulated sequence is rendered to reflect the sensor characteristics of Project Aria glasses, including simulated lens and sensor characteristics. Inertial data is also simulated using a noise model that reflects Project Aria’s IMU sensors.
Each described with a CAD-like language for architectual entities
Architectural features, such as doors, windows, and pillars, are described with a CAD-like language, including the feature type, location, and dimensions. This unlocks new exciting ways to tackle research challenges related to reconstruction and detection tasks.
Realistic simulated trajectory within each environment
Before rendering each sequence, device trajectories are simulated within each environment according to a set of rules that mirror how users walk while wearing Project Aria glasses. Trajectories are created automatically and ensure a full traversal of each virtual scene.
Semi-dense map representation for each scene
In addition to per-frame depth and instance maps for each sequence, semi-dense point cloud representations are also made available for each environment. These additional representations open up new ways for researchers to tackle reconstruction and detection tasks.
Comprehensive tools to load and visualize data easily
Accompanying tools to the Aria Synthetic Environments Dataset allow researchers to interpret the dataset’s CAD-like language, and interactively visualize the data using an interactive 3D floorplan viewer.
We provide a simple python interface to load data, for researchers to get up and running quickly.
Use Aria Synthetic Environments to participate in Open Challenges
Aria Synthetic Environments is designed to catalyze ML research related to scene understanding and reconstruction.
Use the dataset to participate in open benchmark challenges and accelerate progress with the open community.
Access Aria Synthetic Environments Dataset and accompanying Tools
If you are a researcher in AI or ML research, access the Aria Synthetic Environments Dataset and accompanying tools here.
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