Stereo Matching via Disparity Estimation and Surface Modeling
Two new techniques are proposed to improve stereo matching performance in this work. First, to address the disparity discontinuity problem in occluded regions, we present a disparity estimation procedure, which consists of two steps; namely, a greedy disparity filling algorithm and a least-squared-errors (LSE) fitting method. Second, it is observed that the existing fronto-parallel model with color segmentation is built upon the piecewise constant surface approximation, which is however not efficient in approximating slanted or curved objects. We use a piecewise linear surface model to represent 3-dimensional (3D) geometric structure for better surface modeling. The proposed stereo matching system with these two new components is evaluated with Middlebury data sets with excellent quantitative and qualitative results.