Dense image matching is a fundamental low-level problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithms in a consistent way. In the discrete setting, we propose a novel optimization algorithm that can be massively parallelized. In the continuous setting we tackle the problem of non-convex regularizers by a formulation based on differences of convex functions. The resulting hybrid discrete-continuous algorithm can be efficiently accelerated by modern GPUs and we demonstrate its real-time performance for the applications of dense stereo matching and optical flow.
|Number of pages
|Published - 23 Jan 2016
|Computer Vision Winter Workshop: CVWW 2016 - Rimske Toplice, Slovenia
Duration: 3 Feb 2016 → 5 Feb 2016
|Computer Vision Winter Workshop
|3/02/16 → 5/02/16