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ReasonCore/open-spatial-reasoning

Open Spatial Reasoning A multiple-choice dataset of spatial reasoning questions and answers for evaluating 3D spatial reasoning from single driving images. Each image contains numbered bounding boxes referencing objects in the scene, and each question probes a model's ability to reconstruct the real 3D scene rather than rely on flat-image shortcuts (e.g. "lower in the frame = closer", "bigger box = nearer"). Dataset Description Frontier vision-language models… See the full description on the dataset page: https://huggingface.co/datasets/ReasonCore/open-spatial-reasoning.

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Dataset Card

Open Spatial Reasoning

A multiple-choice dataset of spatial reasoning questions and answers for evaluating 3D spatial reasoning from single driving images. Each image contains numbered bounding boxes referencing objects in the scene, and each question probes a model's ability to reconstruct the real 3D scene rather than rely on flat-image shortcuts (e.g. "lower in the frame = closer", "bigger box = nearer").

Dataset Description

Frontier vision-language models often answer these questions correctly by luck while reasoning incorrectly, leaning on pixel-layout heuristics that break down on elevated roads, slopes, curves, and intersections. This dataset is designed to surface that failure mode by requiring metric 3D reasoning about distance, lateral position, ordering, and heading.

Each sample pairs a driving-scene image with a question, four answer choices, and the correct answer letter.

The images were collected by autonomous vehicles operated by PlusAI.

Data Fields

FieldTypeDescription
idstringUnique question identifier (e.g. q0002)
imageimageThe driving image with numbered bounding boxes
categorystringThe reasoning task type (see categories below)
questionstringThe full question, including the reasoning protocol
choiceslist[string]Four answer options, prefixed A.–D.
answerstringThe correct answer letter (A, B, C, or D)

Question Categories

The dataset spans several spatial-reasoning task types, including:

CategoryWhat it tests
identify_distance_longEstimate the absolute distance to an object (binned 0–20m / 20–50m / 50–80m / 80m+)
relative_distance_longEstimate the 3D separation between two objects
pick_closerDecide which of two objects is closer to the ego vehicle
identify_rightmostIdentify the object furthest to the right in true 3D space
order_leftmostOrder several objects left-to-right in 3D space
identify_positionClassify an object's position relative to ego (e.g. ahead-left, behind-right)
identify_headingDetermine an object's heading using clock directions (12 = forward, 3 = right)

Authors

Anurag Ganguli, Anshuman Lall, Abhishek Bhatia, Xiangyu Gao, Joe Yuan, Michael Bosch, Satish Vutukuru, Geoff Wolfe

Citation

If you use this dataset, please cite it:

bibtex
@misc{driving_3d_spatial_reasoning,
  title  = {Open Spatial Reasoning},
  author = {Anurag Ganguli, Anshuman Lall, Abhishek Bhatia, Xiangyu Gao, Joe Yuan, Satish Vutukuru, Geoff Wolfe},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/reasoncore/open-spatial-reasoning}}
}

License

Released under CC BY 4.0. Images were collected by autonomous vehicles operated by PlusAI. ---