Dense Stereo Matching Method Based on Local Affine Model

Abstract

A new method for constructing an accurate disparity space image and performing an efficient cost aggregation in stereo matching based on local affine model is proposed in this paper. The key algorithm includes a new self-adapting dissimilarity measurement used for calculating the matching cost and a local affine model used in cost aggregation stage. Different from the traditional region-based methods, which try to change the matching window size or to calculate an adaptive weight to do the aggregation, the proposed method focuses on obtaining the efficient and accurate local affine model to aggregate the cost volume while preserving the disparity discontinuity. Moreover, the local affine model can be extended to the color space. Experimental results demonstrate that the proposed method is able to provide subpixel precision disparity maps compared with some state-of-the-art stereo matching methods.