TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo

One of the most successful approaches in Multi-View Stereo estimates a depth map and a normal map for each view via PatchMatch-based optimization and fuses them into a consistent 3D points cloud. This relies on photo-consistency to evaluate the goodness of a depth estimate. It generally produces very accurate results, however, the reconstructed model often lacks completeness, especially in correspondence of broad untextured areas where the photo-consistency metrics are unreliable. Assuming the untextured areas piecewise planar, in this paper we generate novel PatchMatch hypotheses so to expand reliable depth estimates in neighboring untextured regions. At the same time, we modify the photo-consistency measure such to favor standard or novel PatchMatch depth hypotheses depending on the textureness of the considered area. Finally, we propose a depth refinement step to filter out wrong estimates and to fill gaps on both the depth and normal maps, while preserving discontinuities. Our method proved its effectiveness against several state of the art algorithms in the publicly available ETH3D dataset containing a wide variety of high and low-resolution images.