PatchMatch based Multi-view Stereo (MVS) algorithms have achieved great
success in large-scale scene reconstruction tasks. However, reconstruction of
texture-less planes often fails as similarity measurement methods may become
ineffective on these regions. Thus, a new plane hypothesis inference strategy
is proposed to handle the above issue. The procedure consists of two steps:
First, multiple plane hypotheses are generated using filtered initial depth
maps on regions that are not successfully recovered; Second, depth hypotheses
are selected using Markov Random Field (MRF). The strategy can significantly
improve the completeness of reconstruction results with only acceptable
computing time increasing. Besides, a new acceleration scheme similar to
dilated convolution can speed up the depth map estimating process with only a
slight influence on the reconstruction. We integrated the above ideas into a
new MVS pipeline, Plane Hypothesis Inference Multi-view Stereo (PHI-MVS). The
result of PHI-MVS is validated on ETH3D public benchmarks, and it demonstrates
competing performance against the state-of-the-art.

本研究提出了一种新的平面假设推理策略，采用多假设生成和马尔可夫随机场选取深度假设的方法，显著提高了大规模场景重建的完整性和性能，在 ETH3D 公共基准测试中取得竞争性成果。