  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 approach 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. We also propose a depth refinement step to filter wrong estimates and to
fill the gaps on both the depth maps and normal maps while preserving the
discontinuities. The effectiveness of our new methods has been tested against
several state of the art algorithms in the publicly available ETH3D dataset
containing a wide variety of high and low-resolution images.
