High-Resolution Multi-View Stereo with Dynamic Depth Edge Flow

Multi-view stereo based on deep learning is mostly dedicated to improving the accuracy of point clouds, whlile complex scenes, occlusion, and other factors limit their reconstruction completeness, especially in the area with drastic changes in depth direction. In this paper, we propose a multi-view stereo network based on depth edge flow (DEF-MVSNet), using the reference image as a guide to dynamically infer the edge coordinates to improve reconstruction completeness. First, we ignore the boundaries in the depth prediction stage to generate better initial depth inference results. Then, we use a EdgeDetect module to extract the obviously features of the reference image and predict the pixel offset of the depth map. Finally, the EdgeFlow module modifies the initial depth map coordinates according to the offset and uses multiple iterations to dynamically update the depth map. The experimental results prove that our method has a great improvement in the completeness of reconstruction compared with MVSNet and R-MVSNet without increasing the memory and time overhead. Code and models are publicly available at https://github.com/linkuizzZ/EF-MVSNet.