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Published in:
2008 | OriginalPaper | Chapter
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
Authors : Rahul Raguram, Jan-Michael Frahm, Marc Pollefeys
Published in: Computer Vision – ECCV 2008
Publisher: Springer Berlin Heidelberg
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The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner that is suitable for real-time implementation. The contributions of this work are two-fold: First, we provide a comparative analysis of the state-of-the-art RANSAC algorithms and categorize the various approaches. Second, we develop a powerful new framework for real-time robust estimation. The technique we develop is capable of efficiently adapting to the constraints presented by a fixed time budget, while at the same time providing accurate estimation over a wide range of inlier ratios. The method shows significant improvements in accuracy and speed over existing techniques.