r3d-bench

R3D-Bench This is the official R3D-Bench dataset from R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables. 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. 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 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: License Derived from the Aria Digital Twin (ADT) dataset; use is subject to the ADT license/terms.