A convex-optimization approach to dense stereo matching
Yujun Li, Oscar C. Au, Lingfeng Xu, Wenxiu Sun, Sung-Him Chui, Chun-Wing Kwok · 2011
We present a novel convex-optimization approach to solving the dense stereo matching problem in computer vision. Instead of directly solving for disparities of pixels, by establishing the connection between a permutation matrix and a disparity vector, we directly formulate the stereo matching problem as a continuous convex quadratic program in a simple, elegant and straightforward manner without performing any complicated relaxation or approximation. By using CVX, the Matlab software for disciplined convex programming, our method is extremely simple to implement.