Towards estimation of dense disparities from stereo images containing large textureless regions

Abstract

Stereo algorithms for structure reconstruction demand accurate disparities with low mismatch errors and false positives. Mismatch errors in large textureless regions force most accurate algorithms to be sparse, with disparities known only in textured regions. We propose a novel method which uses characteristics of the multi-valued disparity to segregate image regions into unambiguous, occluded but textured and regions with low color variation. The disparity in the unambiguous region is calculated using stable matching with local disparity filtering. The disparity is interpolated into other regions by diffusion using unstructured triangulation and method of finite elements for rapid convergence. The boundary conditions for each of the region are appropriately modified so that accurate discontinuities in the disparity are preserved. A comparison of our method with some existing methods through experiments reveal that this algorithm indeed performs significantly better in producing dense accurate disparities.