info dict | categories list | images list | annotations list |
|---|---|---|---|
{"id":23,"source":"COCO","name":"COCO Train","split":"Train","version":"0.1","url":"https://cocodata(...TRUNCATED) | [{"supercategory":"person","id":7,"name":"person"},{"supercategory":"vehicle","id":11,"name":"bicycl(...TRUNCATED) | [{"width":480,"height":640,"file_path":"coco/images/train2017/000000282310.jpg","K":[[1301.835365295(...TRUNCATED) | [{"category_name":"baseball glove","center_cam":[-0.07185069977056613,0.13687015958963705,4.22444565(...TRUNCATED) |
COCO3D
3D bounding box annotations for COCO images, produced by LabelAny3D.
| Split | Images | Annotations | Categories |
|---|---|---|---|
| val | 2,010 | 5,409 | 80 |
| train | 15,869 | 86,395 | 80 |
Format follows Omni3D.
License
CC BY 4.0 covers our 3D annotations only. Images are not redistributed here — they are referenced by COCO image id and file path, and remain under the original COCO terms of use.
Citation
@inproceedings{yao2025labelany3d,
title={LabelAny3D: Label Any Object 3D in the Wild},
author={Jin Yao and Radowan Mahmud Redoy and Sebastian Elbaum and Matthew B. Dwyer and Zezhou Cheng},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2025}
}
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