| --- |
| license: mit |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - spatial-reasoning |
| - 3d |
| - counting |
| - video |
| - benchmark |
| pretty_name: Spatial Scene Bench |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| - config_name: full |
| features: |
| - name: id |
| dtype: string |
| - name: scene_id |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer |
| dtype: string |
| - name: answer_type |
| dtype: string |
| - name: images |
| list: image |
| splits: |
| - name: test |
| num_bytes: 290248515 |
| num_examples: 4387 |
| download_size: 259451429 |
| dataset_size: 290248515 |
| - config_name: mini |
| features: |
| - name: id |
| dtype: string |
| - name: scene_id |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer |
| dtype: string |
| - name: answer_type |
| dtype: string |
| - name: images |
| list: image |
| splits: |
| - name: test |
| num_bytes: 17657343 |
| num_examples: 275 |
| download_size: 10124080 |
| dataset_size: 17657343 |
| configs: |
| - config_name: full |
| data_files: |
| - split: test |
| path: full/test-* |
| - config_name: mini |
| data_files: |
| - split: test |
| path: mini/test-* |
| --- |
| |
| # Spatial Scene Bench |
|
|
| [Code](https://github.com/selfishout/spatial-scene-bench) | [lmms-eval task](https://github.com/EvolvingLMMs-Lab/lmms-eval) |
|
|
| Counting and relational reasoning over procedurally rendered 3D scenes, from single images to 16-frame orbit |
| videos. Every answer is computed from the 3D scene itself, and ambiguous questions (heavily occluded objects, |
| near-ties in depth or distance) are filtered out at generation time. |
|
|
| ## Configs |
|
|
| | Config | Questions | Description | |
| | --- | --- | --- | |
| | `full` | 4,387 | 200 scenes, all 11 question types | |
| | `mini` | 275 | first 25 questions of each type | |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("alithema/spatial-scene-bench", "mini", split="test") |
| ``` |
|
|
| ## Fields |
|
|
| - `question_type`: one of `count`, `count_relation`, `relation`, `compare_count`, `closest`, `distance`, |
| `ego_direction`, `video_count`, `video_closest`, `video_distance`, `video_ego_direction` |
| - `modality`: `image` (one 320×240 view) or `video` (16 orbit frames) |
| - `answer_type`: `count` (integer), `yesno`, `choice` (A–D, options in `options`), or `numeric` (meters) |
| - `images`: the view or the video frames, in order |
|
|
| ## Scoring |
|
|
| Exact match for counts, yes/no and multiple choice; VSI-Bench Mean Relative Accuracy (MRA) for distances. The |
| headline score is the macro average over question types. Reference implementations: `spatial_scene_bench.evaluate` |
| in the code repository and the `spatial_scene_bench` task in lmms-eval. |
|
|
| | Model | Overall (mini) | |
| | --- | --- | |
| | Random baseline | 23.9 | |
| | Qwen2.5-VL-3B-Instruct (zero-shot) | 50.3 | |
|
|
| ## Regenerating |
|
|
| The dataset is fully reproducible from the generator (seed 2025): |
|
|
| ```bash |
| pip install "spatial-scene-bench[export] @ git+https://github.com/selfishout/spatial-scene-bench.git" |
| ssb-generate --out data/ssb --num-scenes 200 --video-frames 16 --seed 2025 |
| python scripts/export_hf.py --data data/ssb --out data/ssb_hf |
| ``` |
|
|
