Robust Sampling Consensus

Abstract

In this thesis, the problem of robust estimation of a multiple view geometry in the computer vision is studied. The main focus is put on random sampling techniques for an estimation of two-view geometries, in particular homography and epipolar geometry, in a presence of outliers. After a thorough analysis of LO-RANSAC, several improvements are proposed to make it more robust to the selection of the inlier/outlier error threshold and to the number of points. A new estimator, faster, more accurate and more robust than the state-of-the-art is the result. The improvements were implemented in the framework of CMP WBS-Demo and extensively tuned and experimentally tested on diverse data, using a newly created testing framework. The LO-RANSAC implementation for homography and epipolar geometry estimation has been separated from the rest of WBS-Demo and is now publicly available. The datasets were made available as well, including new manually annotated ground truth point correspondences.