  This paper presents a novel method, MaskMVS, to solve depth estimation for
unstructured multi-view image-pose pairs. In the plane-sweep procedure, the
depth planes are sampled by histogram matching that ensures covering the depth
range of interest. Unlike other plane-sweep methods, we do not rely on a cost
metric to explicitly build the cost volume, but instead infer a multiplane mask
representation which regularizes the learning. Compared to many previous
approaches, we show that our method is lightweight and generalizes well without
requiring excessive training. We outperform the current state-of-the-art and
show results on the sun3d, scenes11, MVS, and RGBD test data sets.
