Robustly estimating disparity from adaptive dense curves
In this paper, we propose a novel area-based algorithm for stereo correspondence estimation. The new dense correspondence estimation algorithm, which we call HACM (Hierarchical Adaptive Curve Matching), is suitable for stereo analysis of both static and dynamic scenes. The core of this method is the derivation of a pixel-based dense adaptive curve representation. For each pixel in the concerned 2-D images, the changes are characterized roughly in the local surface shape, the texture direction, and the luminance properties, all at a slight overhead cost. The matching is carried out in a hierarchical manner that incorporates several novel ideas that improve its efficiency and robustness. Experiments conducted on both synthetic and natural stereo pairs, and natural video streams, reveal the favorable performance of this new algorithm.
stereo matching disparity estimation image-based rendering 3-d model reconstruction