| --- |
| license: cc-by-nc-4.0 |
| pretty_name: R3D-Bench |
| configs: |
| - config_name: qa_annotations |
| default: true |
| data_files: |
| - split: test |
| path: data/qa_annotations/*.parquet |
| - config_name: gt_bboxes |
| data_files: |
| - split: test |
| path: data/gt_bboxes/*.parquet |
| - config_name: segmentations |
| data_files: |
| - split: test |
| path: data/segmentations/*.parquet |
| - config_name: meshes |
| data_files: |
| - split: test |
| path: data/meshes/*.parquet |
| --- |
| |
| # R3D-Bench |
|
|
| This is the official R3D-Bench dataset from [R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables](https://arxiv.org/abs/2607.02921). |
| R3D-Bench is a benchmark for quantitative 3D spatial reasoning from natural egocentric RGB-D video. |
| 3,033 question-answer annotations over 57 Aria Digital Twin (ADT) sequences. This is an |
| **evaluation-only** benchmark — there is no training split; all data is in the `test` split. |
|
|
| Note that project Aria video data must be downloaded separately. See full setup instructions in our [github repository](https://github.com/facebookresearch/r3d). |
|
|
| ## Configs |
|
|
| - **qa_annotations** (default): one row per question. Fields include `question_text`, |
| `gt_answer`, `question_type`, `sequence_id`, spatial/temporal descriptions, and a nested |
| `referenced_objects` list. This is the benchmark you evaluate against. |
| - **segmentations**: per-frame SAM3 masks (RLE) tracking relevant objects in the scene. Used in R3D-Bench evaluations for most methodologies. These are optional, but useful. |
| - **meshes**: per-object SAM3D reconstructed meshes with the GLB geometry embedded (`glb` bytes), |
| used for evaluating the R3D methodology on R3D-Bench. They are available for use with other methodologies. |
| - **gt_bboxes**: optional. Ground-truth oriented-box trajectories from ADT that the reference |
| answers were derived from. These are mainly for debugging. Evaluations do not use them. |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| qa = load_dataset("facebook/r3d-bench", "qa_annotations", split="test") |
| ``` |
| |
| ## Source video |
| |
| The RGB-D frames are not redistributed here. Obtain the ADT sequences from |
| https://www.projectaria.com/datasets/adt/ and extract frames as described in the R3D code repo. |
| |
| ## Citation |
| |
| When using our work, please cite as follows: |
| |
| ```bibtex |
| @misc{horton2026r3dquantitative3dspatial, |
| title={R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables}, |
| author={Maxwell Horton and Wei Lu and Quan Tran and Yury Astashonok and Kirmani Ahmed and Babak Damavandi and Anuj Kumar and Xiao Zhang and Seungwhan Moon}, |
| year={2026}, |
| eprint={2607.02921}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2607.02921}, |
| } |
| ``` |
| |
| ## License |
| |
| Derived from the Aria Digital Twin (ADT) dataset; use is subject to the ADT license/terms. |
| |
