  Different from most state-of-the-art~(SOTA) algorithms that use static and
uniform sampling methods with a lot of hypothesis planes to get fine depth
sampling. In this paper, we propose a free-moving hypothesis plane method for
dynamic and non-uniform sampling in a wide depth range to build the cost
volume, which not only greatly reduces the number of planes but also finers
sampling, for both of reducing computational cost and improving accuracy, named
Non-Uniform Cost Volume. We present the SuperMVS network to implement
Multi-View Stereo with Non-Uniform Cost Volume. SuperMVS is a coarse-to-fine
framework with four cascade stages. It can output higher resolution and
accurate depth map. Our SuperMVS achieves the SOTA results with low memory, low
runtime, and fewer planes on the DTU datasets and Tanks \& Temples dataset.
