Author

Xiao-Hu Huang

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Preprint Oct 2026

What Makes a Good Visual Representation for Spatial Understanding in MLLMs?

Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual pretraining data, however, is almost entirely 2D, so the visual tokens processed by MLLMs often lack inherent 3D structural information. A growing number of methods inj...

Xiao-Hu Huang, Yu-Ming Guo, Kai Han · 0 citations

Jul 2026

Beyond Single Expert: Harmonizing Diverse Visual Priors in MLLMs for Spatial Understanding

ViPS introduces an Efficient Prior Proxy to generate multiple foundational priors with minimal inference overhead, and a Dynamic Prior Fusion mechanism to achieve harmonious and context-aware prior fusion and injection from the prior proxies.

Xiaofan Lin, Xiaohu Huang, Kai Han · arXiv.org · 0 citations