  To obtain high-resolution depth maps, some previous learning-based multi-view
stereo methods build a cost volume pyramid in a coarse-to-fine manner. These
approaches leverage fixed depth range hypotheses to construct cascaded plane
sweep volumes. However, it is inappropriate to set identical range hypotheses
for each pixel since the uncertainties of previous per-pixel depth predictions
are spatially varying. Distinct from these approaches, we propose a Dynamic
Depth Range Network (DDR-Net) to determine the depth range hypotheses
dynamically by applying a range estimation module (REM) to learn the
uncertainties of range hypotheses in the former stages. Specifically, in our
DDR-Net, we first build an initial depth map at the coarsest resolution of an
image across the entire depth range. Then the range estimation module (REM)
leverages the probability distribution information of the initial depth to
estimate the depth range hypotheses dynamically for the following stages.
Moreover, we develop a novel loss strategy, which utilizes learned dynamic
depth ranges to generate refined depth maps, to keep the ground truth value of
each pixel covered in the range hypotheses of the next stage. Extensive
experimental results show that our method achieves superior performance over
other state-of-the-art methods on the DTU benchmark and obtains comparable
results on the Tanks and Temples benchmark. The code is available at
https://github.com/Tangshengku/DDR-Net.
