  In this paper, we propose an efficient multi-scale geometric consistency
guided multi-view stereo method for accurate and complete depth map estimation.
We first present our basic multi-view stereo method with Adaptive Checkerboard
sampling and Multi-Hypothesis joint view selection (ACMH). It leverages
structured region information to sample better candidate hypotheses for
propagation and infer the aggregation view subset at each pixel. For the depth
estimation of low-textured areas, we further propose to combine ACMH with
multi-scale geometric consistency guidance (ACMM) to obtain the reliable depth
estimates for low-textured areas at coarser scales and guarantee that they can
be propagated to finer scales. To correct the erroneous estimates propagated
from the coarser scales, we present a novel detail restorer. Experiments on
extensive datasets show our method achieves state-of-the-art performance,
recovering the depth estimation not only in low-textured areas but also in
details.
