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Liyong Wang

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NeurIPS Conference 2025 Conference Paper

MVSMamba: Multi-View Stereo with State Space Model

Jianfei Jiang
Qiankun Liu
Hongyuan Liu
Haochen Yu
Liyong Wang
Jiansheng Chen
Huimin Ma

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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AAAI Conference 2025 Conference Paper

RRT-MVS: Recurrent Regularization Transformer for Multi-View Stereo

Jianfei Jiang
Liyong Wang
Haochen Yu
Tianyu Hu
Jiansheng Chen
Huimin Ma

Learning-based multi-view stereo methods aim to predict depth maps for reconstructing dense point clouds. These methods rely on regularization to reduce redundancy in the cost volume. However, existing methods have limitations: CNN-based regularization is restricted to local receptive fields, while Transformer-based regularization struggles with handling depth discontinuities. These limitations often result in inaccurate depth maps with significant noise, particularly noticeable in the boundary and background regions. In this paper, we propose a Recurrent Regularization Transformer for Multi-View Stereo (RRT-MVS), which addresses these limitations by regularizing the cost volume separately for depth and spatial dimensions. Specifically, we introduce Recurrent Self-Attention (R-SA) to aggregate global matching costs within and across the cost maps and filter out noisy feature correlations. Additionally, we present Depth Residual Attention (DRA) to aggregate depth correlations within the cost volume and a Positional Adapter (PA) to enhance 3D positional awareness in each 2D cost map, further augmenting the effectiveness of R-SA. Experimental results demonstrate that RRT-MVS achieves state-of-the-art performance on the DTU and Tanks-and-Temples datasets. Notably, RRT-MVS ranks first on both the Tanks-and-Temples intermediate and advanced benchmarks among all published methods.

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