Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
This work addresses a fundamental computer vision problem with potential applications in robotics and augmented reality, though it appears incremental as it builds on existing optimization methods.
The paper tackled dense image matching by combining discrete and continuous optimization into a hybrid framework, achieving real-time performance on GPUs for stereo matching and optical flow applications.
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.