Multi-View Stereo Using Graph Cuts-Based Depth Refinement.

Multi-View Stereo (MVS) methods tackle the ill-posed inverse problem of recovering an object's 3D structure from its multi-view calibrated images. High computational cost restricts most MVS methods from using global information for depth estimation. We present a depth map-based MVS method that uses global information to estimate the depths of all pixels in an image simultaneously. To this end, we transform the depth refinement problem into computing max-flow/min-cut on a 3D grid graph with offset vertices. The $s{-}t$ min-cut of this graph corresponds to the minimization of an energy functional consisting of photo-consistency and smoothness terms. Experimental results on indoor and outdoor datasets validate the efficacy of our method, especially on models with low textured regions where global information is necessary to infer the correct depth. [ABSTRACT FROM AUTHOR]