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Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models

Xingrui Wang¹, Wufei Ma¹, Tiezheng Zhang¹, Celso M de Melo², Jieneng Chen¹, Alan Yuille¹

¹ Johns Hopkins University    ² DEVCOM Army Research Laboratory

Project Page / Paper / 🤗 Huggingface / Code

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🧠 Introduction

Spatial457 is a diagnostic benchmark designed to evaluate the 6D spatial reasoning capabilities of large multimodal models (LMMs). It systematically introduces four key capabilities—multi-object understanding, 2D and 3D localization, and 3D orientation—across five difficulty levels and seven question types, progressing from basic recognition to complex physical interaction.

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📦 Download

You can access the full dataset and evaluation toolkit:

• Dataset: Hugging Face
• Code: GitHub Repository
• Paper: arXiv 2502.08636

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📊 Benchmark

We benchmarked a wide range of state-of-the-art models—including GPT-4o, Gemini, Claude, and several open-source LMMs—on all subsets. Performance consistently drops as task difficulty increases. PO3D-VQA and humans remain most robust across all levels.

The table below summarizes model performance across 7 subsets:

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📚 Citation

@inproceedings{wang2025spatial457,
  title     = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models},
  author    = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2025},
  url       = {https://arxiv.org/abs/2502.08636}
}