  PatchMatch Multi-View Stereo (PatchMatch MVS) is one of the popular MVS
approaches, owing to its balanced accuracy and efficiency. In this paper, we
propose Polarimetric PatchMatch multi-view Stereo (PolarPMS), which is the
first method exploiting polarization cues to PatchMatch MVS. The key of
PatchMatch MVS is to generate depth and normal hypotheses, which form local 3D
planes and slanted stereo matching windows, and efficiently search for the best
hypothesis based on the consistency among multi-view images. In addition to
standard photometric consistency, our PolarPMS evaluates polarimetric
consistency to assess the validness of a depth and normal hypothesis, motivated
by the physical property that the polarimetric information is related to the
object's surface normal. Experimental results demonstrate that our PolarPMS can
improve the accuracy and the completeness of reconstructed 3D models,
especially for texture-less surfaces, compared with state-of-the-art PatchMatch
MVS methods.
