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:
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
