MVSMamba: Multi-View Stereo with State Space Model
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917055500910592 |
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| author | Jiang, Jianfei Liu, Qiankun Liu, Hongyuan Yu, Haochen Wang, Liyong Chen, Jiansheng Ma, Huimin |
| author_facet | Jiang, Jianfei Liu, Qiankun Liu, Hongyuan Yu, Haochen Wang, Liyong Chen, Jiansheng Ma, Huimin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MVSMamba: Multi-View Stereo with State Space Model Jiang, Jianfei Liu, Qiankun Liu, Hongyuan Yu, Haochen Wang, Liyong Chen, Jiansheng Ma, Huimin Computer Vision and Pattern Recognition 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. |
| title | MVSMamba: Multi-View Stereo with State Space Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.01315 |