README.md

---
license: cc-by-4.0
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - spatial-reasoning
  - vision-language-models
  - benchmark
  - 3d
  - occlusion
  - perspective
  - abstention
size_categories:
  - 10K<n<100K
pretty_name: SpatialUncertain
---

# SpatialUncertain

A controlled 3D benchmark evaluating whether Vision-Language Models (VLMs) know
**when not to answer** spatial reasoning questions under occlusion and
perspective ambiguity.

Companion to the paper *Seeing Isn't Knowing: Do VLMs Know When Not to Answer
Spatial Questions (and Why)?*

## Structure

```
SpatialUncertain/
├── questions/
│   ├── occlusion_benchmark.json         # ~6,600 questions
│   ├── perspective_benchmark.json       # ~3,700 questions
│   └── view_selection_benchmark.json    # 670 questions
└── scenes/                              # downloaded from the tar.gz files below
    ├── clean_scenes/                    # 555 base scenes
    ├── occlusion_scenes/                # 272 occluded variants
    ├── perspective_scenes/              # 505 perspective-ambiguous variants
    └── view_selection_scenes/           # 505 multi-view variants
```

Each scene folder contains:
- `multiview/view_XXX.png` — 8 rendered viewpoints
- `camera_poses.json` — per-view camera parameters
- `object_attributes.json` — object metadata
- `structure_proxy.json`, `target_subgraph.json` — scene-graph annotations
- `walkthrough.mp4` — a short walk-through render

## Files

| File | Size | Contents |
|---|---|---|
| `questions/*.json` | 17 MB | All questions, ground-truth answers, condition tags |
| `clean_scenes.tar.gz` | … | Base scenes (no challenge) |
| `occlusion_scenes.tar.gz` | … | Occlusion variants |
| `perspective_scenes.tar.gz` | … | Perspective-ambiguity variants |
| `view_selection_scenes.tar.gz` | … | View-selection variants |

## Quick start

```python
from huggingface_hub import snapshot_download
# Questions only (small, ~17 MB)
snapshot_download("zhangyuejoslin/SpatialUncertain",
                  repo_type="dataset",
                  allow_patterns="questions/*")

# Full dataset (large)
snapshot_download("zhangyuejoslin/SpatialUncertain", repo_type="dataset")
```

After downloading, extract the tars:
```bash
for f in *_scenes.tar.gz; do tar -xzf "$f"; done
```

## License

Released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
Please cite the paper if you use this benchmark.

## Citation

```bibtex
@article{zhang2026spatialuncertain,
  title  = {Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?},
  author = {Zhang, Yue and Wang, Zun and Lin, Han and Bitton, Yonatan and Szpektor, Idan and Bansal, Mohit},
  journal= {arXiv preprint arXiv:XXXX.XXXXX},
  year   = {2026}
}
```