  Recent work in multi-view stereo (MVS) combines learnable photometric scores
and regularization with PatchMatch-based optimization to achieve robust
pixelwise estimates of depth, normals, and visibility. However, non-learning
based methods still outperform for large scenes with sparse views, in part due
to use of geometric consistency constraints and ability to optimize over many
views at high resolution. In this paper, we build on learning-based approaches
to improve photometric scores by learning patch coplanarity and encourage
geometric consistency by learning a scaled photometric cost that can be
combined with reprojection error. We also propose an adaptive pixel sampling
strategy for candidate propagation that reduces memory to enable training on
larger resolution with more views and a larger encoder. These modifications
lead to 6-15% gains in accuracy and completeness on the challenging ETH3D
benchmark, resulting in higher F1 performance than the widely used
state-of-the-art non-learning approaches ACMM and ACMP.
