Building a foundation model for 3D vision is a complex challenge that remains unsolved. Towards that goal, it is important to understand the 3D reasoning capabilities of current models as well as identify the gaps between these models and humans. Therefore, we construct a new 3D visual understanding benchmark named UniQA-3D. UniQA-3D covers fundamental 3D vision tasks in the Visual Question Answering (VQA) format. We evaluate state-of-the-art Vision-Language Models (VLMs), specialized models, and human subjects on it. Our results show that VLMs generally perform poorly, while the specialized models are accurate but not robust, failing under geometric perturbations. In contrast, human vision continues to be the most reliable 3D visual system. We further demonstrate that neural networks align more closely with human 3D vision mechanisms compared to classical computer vision methods, and Transformer-based networks such as ViT align more closely with human 3D vision mechanisms than CNNs. We hope our study will benefit the future development of foundation models for 3D vision. Code is available at https://github.com/princeton-vl/UniQA-3D .
@article{arxiv.2410.10799,
title = {Towards Foundation Models for 3D Vision: How Close Are We?},
author = {Yiming Zuo and Karhan Kayan and Maggie Wang and Kevin Jeon and Jia Deng and Thomas L. Griffiths},
journal= {arXiv preprint arXiv:2410.10799},
year = {2024}
}
Comments
Accepted to 3DV 2025. Update 12/09/24: Change the benchmark name to UniQA-3D, add link to code