  Existing learning-based multi-view stereo (MVS) methods rely on the depth
range to build the 3D cost volume and may fail when the range is too large or
unreliable. To address this problem, we propose a disparity-based MVS method
based on the epipolar disparity flow (E-flow), called DispMVS, which infers the
depth information from the pixel movement between two views. The core of
DispMVS is to construct a 2D cost volume on the image plane along the epipolar
line between each pair (between the reference image and several source images)
for pixel matching and fuse uncountable depths triangulated from each pair by
multi-view geometry to ensure multi-view consistency. To be robust, DispMVS
starts from a randomly initialized depth map and iteratively refines the depth
map with the help of the coarse-to-fine strategy. Experiments on DTUMVS and
Tanks\&Temple datasets show that DispMVS is not sensitive to the depth range
and achieves state-of-the-art results with lower GPU memory.
