HQP-MVS:High-Quality Plane Priors Assisted Multi-View Stereo for Low-Textured Areas

Abstract

The completeness of reconstructed models in low-textured areas in multi-view stereo is still a challenging problem because of the unreliable photometric consistency. Since these areas always exhibit planar properties, many methods explicitly construct planar priors to assist in optimizing depth estimation. However, the planar models they constructed are not robust enough, and the plane parameters for the same region are not consistent in different views. In this paper, we develop a novel framework to obtain high-quality planar priors. Specifically, we first propose an efficient credible point selection method. We then combine neighboring views to generate a sparse point cloud, utilize multi-planes detection and produce planar prior for each view. Therefore different views can obtain the same plane information of the considered low-textured regions in our method. Finally, we embed our novel planar prior into PatchMatch MVS to get the final depth maps. Experiments on the ETH3D datasets show our method reconstruct 3D model in low-textured areas depth estimation effectively in untextured areas.