Learning Deformable Hypothesis Sampling for Accurate PatchMatch
  Multi-View Stereo
Hongjie Li Yao Guo Xianwei Zheng Hanjiang Xiong
This paper introduces a learnable Deformable Hypothesis Sampler
(DeformSampler) to address the challenging issue of noisy depth estimation for
accurate PatchMatch Multi-View Stereo (MVS). We observe that the heuristic
depth hypothesis sampling modes employed by PatchMatch MVS solvers are
insensitive to (i) the piece-wise smooth distribution of depths across the
object surface, and (ii) the implicit multi-modal distribution of depth
prediction probabilities along the ray direction on the surface points.
Accordingly, we develop DeformSampler to learn distribution-sensitive sample
spaces to (i) propagate depths consistent with the scene's geometry across the
object surface, and (ii) fit a Laplace Mixture model that approaches the
point-wise probabilities distribution of the actual depths along the ray
direction. We integrate DeformSampler into a learnable PatchMatch MVS system to
enhance depth estimation in challenging areas, such as piece-wise discontinuous
surface boundaries and weakly-textured regions. Experimental results on DTU and
Tanks \& Temples datasets demonstrate its superior performance and
generalization capabilities compared to state-of-the-art competitors. Code is
available at https://github.com/Geo-Tell/DS-PMNet.