Issue No. 08 - Aug. (2013 vol. 35)
Rahul Raguram , University of North Carolina at Chapel Hill, Chapel Hill
Ondrej Chum , Czech Technical University, Prague
Marc Pollefeys , ETH Zurich, Zurich
Jiri Matas , Czech Technical University, Prague
Jan-Michael Frahm , University of North Carolina at Chapel Hill, Chapel Hill
A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing and comparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.
Computational modeling, Robustness, Estimation, Data models, Standards, Algorithm design and analysis, Context, robust estimation, RANSAC
J. Frahm, J. Matas, M. Pollefeys, O. Chum and R. Raguram, "USAC: A Universal Framework for Random Sample Consensus," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 2022-2038, 2013.