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Hop-Diffusion Monte Carlo for Epipolar Geometry Estimation between Very Wide-Baseline Images
March 2013 (vol. 35 no. 3)
pp. 755-762
Aveek S. Brahmachari, University of South Florida, Tampa
Sudeep Sarkar, University of South Florida, Tampa
We present a Monte Carlo approach for epipolar geometry estimation that efficiently searches for minimal sets of inlier correspondences in the presence of many outliers in the putative correspondence set, a condition that is prevalent when we have wide baselines, significant scale changes, rotations in depth, occlusion, and repeated patterns. The proposed Monte Carlo algorithm uses Balanced LOcal and Global Search (BLOGS) to find the best minimal set of correspondences. The local search is a diffusion process using Joint Feature Distributions that captures the dependencies among the correspondences. And, the global search is a hopping search process across the minimal set space controlled by photometric properties. Using a novel experimental protocol that involves computing errors for manually marked ground truth points and images with outlier rates as high as 90 percent, we find that BLOGS is better than related approaches such as MAPSAC [1], NAPSAC [2], and BEEM [3]. BLOGS results are of similar quality as other approaches, but BLOGS generate them in 10 times fewer iterations. The time per iteration for BLOGS is also the lowest among the ones we studied.
Index Terms:
Geometry,Proposals,Blogs,Joints,Estimation,Linear programming,Monte Carlo methods,Joint Feature Distribution (JFD),Epipolar geometry,Markov Chain Monte Carlo (MCMC)
Citation:
Aveek S. Brahmachari, Sudeep Sarkar, "Hop-Diffusion Monte Carlo for Epipolar Geometry Estimation between Very Wide-Baseline Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 755-762, March 2013, doi:10.1109/TPAMI.2012.227
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