Geometric Prior-Guided Self-Supervised Learning for Multi-View Stereo

Abstract

Recently, self-supervised multi-view stereo (MVS) methods, which are dependent primarily on optimizing networks using photometric consistency, have made clear progress. However, the difference in lighting between different views and reflective objects in the scene can make photometric consistency unreliable. To address this issue, a geometric prior-guided multi-view stereo (GP-MVS) for self-supervised learning is proposed, which exploits the geometric prior from the input data to obtain high-quality depth pseudo-labels. Specifically, two types of pseudo-labels for self-supervised MVS are proposed, based on the structure-from-motion (SfM) and traditional MVS methods. One converts the sparse points of SfM into sparse depth maps and combines the depth maps with spatial smoothness constraints to obtain a sparse prior loss. The other generates initial depth maps for semi-dense depth pseudo-labels using the traditional MVS, and applies a geometric consistency check to filter the wrong depth in the initial depth maps. We conducted extensive experiments on the DTU and Tanks and Temples datasets, which demonstrate that our method achieves state-of-the-art performance compared to existing unsupervised/self-supervised approaches, and even performs on par with traditional and supervised approaches.