This work considers the problem of estimating the epipolar geometry between two cameras without needing a prespecified set of correspondences. It is capable of resolving the epipolar geometry for cases when the views differ significantly in terms of baseline and rotation, resulting in a large number features in one image that have no correspondence in the other image. We do conditional characterization of the probability space of correspondences based on Joint Feature Distributions (JFD) [21]. We seek to maximize the probabilistic support of the putative correspondence set over a number of MCMC iterations, guided by proposal distributions based on similarity or JFD. Similarity based guidance provides large movements (global) through correspondence space and JFD based guidance provides small movements (local) around the best known epipolar geometry the algorithm has found so far. We also propose a simple and novel method to rule out, at each iteration, correspondences that lead to dege...
Aveek S. Brahmachari, Sudeep Sarkar