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, NAPSAC, and BEEM. 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.

We propose an algorithm for detection of noisy GPS and magnetometer tags in wide-baseline camera views. Our algorithm neither needs densely sampled views nor does it need a single visually connected path through all the views in the dataset. We use vision-based estimates of mutual rotation and translation between cameras to compute a measure of confidence on the correctness of the associated GPS and magnetometer tags. The vision algorithm can find the epipolar geometry between two wide-baseline images without needing pre-specified correspondences. We have two versions of our approach; one that requires geometric pose estimation between all pairs of images and a faster version that uses a pre-filter based on photometric comparison of images to quickly reject non-overlapping views from further geometric consideration. We show qualitative and quantitative results on the Nokia Grand Challenge 2010 Dataset. We find that magnetometer readings are more accurate than GPS readings.

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). 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 degenerate configurations, thus speeding up convergence. We compare our algorithm with LO-RANSAC, NAPSAC, MAPSAC and BEEM, which are the current state of the art competing methods, on a dataset that has significantly more change in baseline, rotation, and scale than those used in the current literature. We quantitatively benchmark the performance using manually specified ground truth corresponding point pairs. We find that our approach can achieve results of similar quality as the current state of art in 10 times lesser number of iterations. We are also able to tolerate upto 90% outlier correspondences.