edatai/spatiolm-depth

SpatioLM Depth Benchmark

This dataset contains the processed depth and spatial-relation benchmark used by SpatioLM. It is distributed in the Hugging Face DatasetDict.save_to_disk format to remain directly compatible with the evaluation tasks included in the SpatioLM repository.

Dataset structure

Split	Examples	Description
single_view	1,600	Single-image metric depth samples derived from SUN RGB-D, NYUv2, and Waymo.
multi_view	1,492	Paired-view metric depth samples derived from NRGBD, ScanNet v2, and KITTI.
relate_task	2,800	Depth-related reasoning over speed/time, two-point distance, camera pose, and cross-view geometry.

Images and depth maps are embedded in the Arrow files. Depending on the split, examples additionally contain sampled pixel coordinates, camera-space depth, Euclidean distance, camera intrinsics, and camera poses.

Download and load

Download the complete repository without changing its directory structure:

hf download edatai/spatiolm-depth \
  --repo-type dataset \
  --local-dir data/eval/spatiolm_depth

Load it with datasets.load_from_disk:

from datasets import load_from_disk

dataset = load_from_disk("data/eval/spatiolm_depth")
print(dataset)

The repository is intentionally not converted to Hub-native Parquet. Use load_from_disk, not load_dataset, to preserve compatibility with the SpatioLM evaluation task definitions.

SpatioLM evaluation tasks

Task	Split
spatiolm_depth_sv	single_view
spatiolm_depth_mv	multi_view
spatiolm_depth_mt	relate_task

See the SpatioLM repository for the model adapter, metrics, and evaluation commands.

Source data and licensing

This is a processed benchmark derived from SUN RGB-D, NYUv2, Waymo, NRGBD, ScanNet v2, and KITTI. The images, depth maps, calibration data, and derived annotations remain subject to the licenses and terms of their respective source datasets. No single permissive license is asserted for the combined benchmark. Users are responsible for complying with all applicable upstream terms.

Citation

@inproceedings{wu2026spatiolm,
  title={SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models},
  author={Wu, Jing and Wu, Jianhua and Guan, Jiayi and Chen, Jiahong and Lu, Jinghui and Ye, Hangjun and Gao, Bingzhao and Chen, Long},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2026},
  note={To appear},
  eprint={2608.01899},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2608.01899}
}