README.md

metadata

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 | lmms-eval task

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

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):

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