MVSMamba: Multi-View Stereo with State Space Model

• URL: http://arxiv.org/abs/2511.01315v1
• Date: Mon, 03 Nov 2025 07:59:07 GMT
• Title: MVSMamba: Multi-View Stereo with State Space Model
• Authors: Jianfei Jiang, Qiankun Liu, Hongyuan Liu, Haochen Yu, Liyong Wang, Jiansheng Chen, Huimin Ma,
• Abstract summary: We propose MVSMamba, the first Mamba-based Multi-View Stereo network.<n>MVSMamba enables efficient global feature aggregation with minimal computational overhead.<n>We show MVSMamba outperforms state-of-the-art MVS methods on the DTU dataset and the Tanks-and-Temples benchmark.
• Score: 27.77454663421622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust feature representations are essential for learning-based Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capture long-range dependencies based on local features extracted by conventional feature pyramid networks. However, the quadratic complexity of Transformer-based MVS methods poses challenges to balance performance and efficiency. Motivated by the global modeling capability and linear complexity of the Mamba architecture, we propose MVSMamba, the first Mamba-based MVS network. MVSMamba enables efficient global feature aggregation with minimal computational overhead. To fully exploit Mamba's potential in MVS, we propose a Dynamic Mamba module (DM-module) based on a novel reference-centered dynamic scanning strategy, which enables: (1) Efficient intra- and inter-view feature interaction from the reference to source views, (2) Omnidirectional multi-view feature representations, and (3) Multi-scale global feature aggregation. Extensive experimental results demonstrate MVSMamba outperforms state-of-the-art MVS methods on the DTU dataset and the Tanks-and-Temples benchmark with both superior performance and efficiency. The source code is available at https://github.com/JianfeiJ/MVSMamba.

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