Solving dense stereo matching via quadratic programming

We study the problem of formulating the discrete dense stereo matching using continuous convex optimization. One of the previous work derived a relaxed convex formulation by establishing the relationship between the disparity vector and a warping matrix. However it suffers from high computational co...

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Bibliographic Details
Published in2014 IEEE Visual Communications and Image Processing Conference pp. 370 - 373
Main Authors Rui Ma, Au, Oscar C., Pengfei Wan, Wenxiu Sun, Lingfeng Xu, Luheng Jia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Accuracy
dense stereo matching
Laplace equations
Matrix converters
Quadratic programming
Stereo vision
Vectors
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Summary:We study the problem of formulating the discrete dense stereo matching using continuous convex optimization. One of the previous work derived a relaxed convex formulation by establishing the relationship between the disparity vector and a warping matrix. However it suffers from high computational complexity. In this paper, the previous convex formulation is translated into an equivalent quadratic programming (QP). Then redundant variables and constraints are eliminated by exploiting the internal sparse property of the warping matrix. The resulting QP can be efficiently tackled using interior point solvers. Moreover, enhanced smoothness term and effective post-processing procedures are also incorporated to further improve the disparity accuracy. Experimental results show that the proposed method is much faster and better than the previous convex formulation, and provides competitive results against existing convex approaches.
DOI:10.1109/VCIP.2014.7051583