| --- |
| pretty_name: SpatioLM Depth Benchmark |
| license: other |
| task_categories: |
| - depth-estimation |
| - visual-question-answering |
| tags: |
| - spatial-reasoning |
| - metric-depth |
| - multi-view |
| - camera-geometry |
| - spatiolm |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # SpatioLM Depth Benchmark |
|
|
| This dataset contains the processed depth and spatial-relation benchmark used by |
| [SpatioLM](https://github.com/xiaomi-research/spatio-lm). 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: |
|
|
| ```bash |
| hf download edatai/spatiolm-depth \ |
| --repo-type dataset \ |
| --local-dir data/eval/spatiolm_depth |
| ``` |
|
|
| Load it with `datasets.load_from_disk`: |
|
|
| ```python |
| 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](https://github.com/xiaomi-research/spatio-lm) 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
