Balanced Exploration and Exploitation Model Search for Efficient Epipolar Geometry Estimation

The estimation of the epipolar geometry is especially difficult when the putative correspondences include a low percentage of inlier correspondences and/or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. This work presents the balanced exploration and exploitation model (BEEM) search algorithm, which works very well especially for these difficult scenes. The algorithm handles these two problems in a unified manner. It includes the following main features: 1) balanced use of three search techniques: global random exploration, local exploration near the current best solution, and local exploitation to improve the quality of the model, 2) exploitation of available prior information to accelerate the search process, 3) use of the best found model to guide the search process, escape from degenerate models, and define an efficient stopping criterion, 4) presentation of a simple and efficient method to estimate the epipolar geometry from two scale-invariant feature transform (SIFT) correspondences, and 5) use of the locality-sensitive hashing (LSH) approximate nearest neighbor algorithm for fast putative correspondence generation. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the state-of-the-art algorithms.