  Traditional MVS methods have good accuracy but struggle with completeness,
while recently developed learning-based multi-view stereo (MVS) techniques have
improved completeness except accuracy being compromised. We propose depth
discontinuity learning for MVS methods, which further improves accuracy while
retaining the completeness of the reconstruction. Our idea is to jointly
estimate the depth and boundary maps where the boundary maps are explicitly
used for further refinement of the depth maps. We validate our idea and
demonstrate that our strategies can be easily integrated into the existing
learning-based MVS pipeline where the reconstruction depends on high-quality
depth map estimation. Extensive experiments on various datasets show that our
method improves reconstruction quality compared to baseline. Experiments also
demonstrate that the presented model and strategies have good generalization
capabilities. The source code will be available soon.
