INTRODUCING

Aria Everyday Objects (AEO)

A small-scale, real-world Project Aria dataset
with high quality static 3D oriented bounding boxes annotation

WHAT IS IT?

A new validation dataset for egocentric benchmark tasks

Research projects, like EFM3D and SceneScript, demonstrate the growing potential of using synthetic data for training large AI models. However, it's crucial to validate these models in real-world environments to ensure they effectively generalize beyond simulations.

The AEO dataset serves as a challenging real-world validation tool for 3D object detection in egocentric data, ensuring models trained in simulations can perform reliably in real-world scenarios.

DOWNLOAD THE DATASET BELOW

EXPLORE THE DATA IN ARIA DATASET EXPLORER

Dataset Contents

Project Aria glasses data (including 2 x SLAM cameras, 1 x RGB camera, 2 x IMU, and complete sensor calibrations)
Aria machine perception service annotations including semi-dense point clouds and 6DoF device trajectory.
Manually-annotated 3D object bounding boxes

Sequence Metrics

45 minutes of egocentric recordings captured by non-computer vision experts across 25 diverse, primarily indoor, real-world environments
1037 3D object bounding box instances across 17 classes: Bed, Chair, Couch, Door Floor, Lamp, Mirror, Plant, Refrigerator, Screen, Sink, Storage, Table, Wall, WallArt, WasherDryer, and Window

How is AEO used?

Used for validating egocentric foundation models, with EFM3D

AEO is used as a validation dataset in the EFM3D benchmark, the first baseline for egocentric 3d foundation models that are optimized for egocentric devices with robust 3D priors.

LEARN MORE ABOUT EFM3D

A visualization of a point cloud and trajectory from two Project Aria devices.

Read the EFM3D Research Paper

For more information about EFM3D benchmark and the first egocentric foundation model, EVL, read our paper here.

READ THE EFM3D RESEARCH PAPER

A screenshot from the EFM3D research paper.

BibTex Citation

@article{straub24efm,
      title={EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models},
      author={Julian Straub and Daniel DeTone and Tianwei Shen and Nan Yang and Chris Sweeney and Richard Newcombe},
      booktitle={arXiv preprint arXiv:2406.10224},
      year={2024},
      url={https://arxiv.org/abs/2406.10224},
}

Access AEO Dataset and accompanying Tools

If you are a researcher in AI or ML research, access the AEO Dataset and accompanying tools here.

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