Toward End-to-End Underwater Multi-View Stereo for Real-World Dense Scene Reconstruction

Multi-view stereo (MVS) enables accurate and complete 3D reconstruction from multi-view imagery, serving as a core methodology in remote sensing applications across terrestrial and underwater domains. Recent advancements in learning-based MVS have demonstrated significant improvements over traditional counterparts, primarily due to the extensive availability of multi-view training images with ground-truth metric depths in the terrestrial in-air domain. However, underwater multi-view stereo (UwMVS) faces substantial challenges arising from the domain gap between in-air and underwater environments, leading to degraded performance when applying in-air MVS models to underwater scenarios. Furthermore, the progress of learning-based UwMVS methods has been hindered by the scarcity of underwater multi-view images with ground-truth depth maps and point clouds. In this paper, we address these challenges by introducing a physically-guided approach for synthesizing underwater multi-view images and presenting the first large-scale synthetic UwMVS dataset preserving real-world underwater degradation properties for end-to-end training and evaluation of learning-based UwMVS methods. Furthermore, we propose a novel UwMVS network that enhances geometric cue encoding to achieve more accurate and complete point cloud reconstruction. Extensive experiments on the dataset and real-world underwater scenes demonstrate that our dataset enables the trained models for underwater dense reconstruction and that our method achieves state-of-the-art performance in underwater reconstruction. Dataset, appendix, and supplementary video are available at https://yang-sober.github.io/UnderMVS/.