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PatchMatch based Multi-view Stereo (MVS) algorithms have achieved greatsuccess in large-scale scene reconstruction tasks. However, reconstruction oftexture-less planes often fails as similarity measurement methods may becomeineffective on these regions. Thus, a new plane hypothesis inference strategyis proposed to handle the above issue. The procedure consists of two steps:First, multiple plane hypotheses are generated using filtered initial depthmaps on regions that are not successfully recovered; Second, depth hypothesesare selected using Markov Random Field (MRF). The strategy can significantlyimprove the completeness of reconstruction results with only acceptablecomputing time increasing. Besides, a new acceleration scheme similar todilated convolution can speed up the depth map estimating process with only aslight influence on the reconstruction. We integrated the above ideas into anew MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). Theresult of PHI-MVS is validated on ETH3D public benchmarks, and it demonstratescompeting performance against the state-of-the-art.

PHI-MVS: Plane Hypothesis Inference Multi-view Stereo for Large-Scale Scene Reconstruction