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

Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity