WL-MVSNet: Frequency-Aware and Regularized Learning for Multi-View Stereo

In recent years, learning-based Multi-View Stereo (MVS) methods have gained increasing attention due to their superior feature representation capabilities and end-to-end optimization frameworks. However, most existing approaches encounter significant challenges in capturing high-frequency details and preserving intricate edge structures, which often leads to suboptimal reconstruction results. To address these limitations, we propose WL-MVSNet, a novel framework designed to enhance matching precision and improve depth estimation performance. Specifically, we propose a wavelet-domain feature refinement strategy that extracts more salient features and incorporate a high-frequency clipping approach to mitigate noise interference, thereby enhancing reconstruction accuracy. Moreover, we introduce a Laplacian operator-based loss function to explicitly constrain edge details in depth maps, thus enhancing the accuracy of depth estimation along object boundaries. Experimental results demonstrate that WL-MVSNet significantly improves the robustness of MVS methods in complex scenarios, especially excelling in weakly-textured regions and along edges.