SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding (CVPR 2026 Highlight)

This repository contains the official PyTorch implementation of SpatialScore: https://arxiv.org/abs/2505.17012/.

Our new version paper has been accepted by CVPR 2026, and we have updated our up-to-date code and data! Feel free to reach out for discussions!

Current Leaderboard (You are welcome to test your models on SpatialScore!):

Some Information

Project Page · Paper · SpatialScore_Benchmark · SpatialCorpus · Model

News

• [2026.5] We have updated our up-to-date code and data!
• [2026.4] Glad to share that SpatialScore has been accepted to CVPR 2026 and selected as Highlight.
• [2025.5] We have released version_0 of our evaluation code, supporting most mainstream models.
• [2025.5] We have released version_0 of SpatialScore, which is available on Huggingface.
• [2025.5] Our pre-print paper is released on arXiv.

Requirements

• Python >= 3.10 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 2.8.0
• accelerate == 1.13.0
• xformers==0.0.32.post1
• flash-attn==2.8.2
• vllm == 0.11.0
• triton == 3.4.0
• triton_kernels (please refer to gpt_oss for version supporting gpt_oss)
• transformers == 4.57.3

The aforementioned dependencies are necessary for conducting evaluations on SpatialScore. If you intend to utilize SpatialAgent; since it requires invoking various spatial perception tools, you may need to consult the following repositories to install the corresponding tool dependencies, and download their corresponding pre-trained checkpoints, including Rex-Omni, Map-Anything, GroundingDINO and DetAny3D.

A suitable conda environment named SpatialScore can be created and activated with:

conda env create -f environment.yaml
conda activate SpatialScore

Citation

If you use this code, model, and data for your research or project, please cite:

@inproceedings{wu2026spatialscore,
  author    = {Wu, Haoning and Huang, Xiao and Chen, Yaohui and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  title     = {SpatialScore: Towards Comprehensive Evaluation for Spatial Intelligence},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year    = {2026},

}

TODO

• Release Paper
• Update the final version paper
• Release version_0 SpatialScore Benchmark
• Release version_0 Code of Evaluation
• Release version_0 Base Code of SpatialAgent
• Release our training resources SpatialCorpus and the SFT models
• Update SpatialScore Benchmark
• Update Code of Evaluation
• Update Code of SpatialAgent

Acknowledgements

Many thanks to the code bases from transformers, Qwen3-VL, and TACO.

Contact

If you have any questions, please feel free to contact haoningwu3639@gmail.com.