USAC: A Universal Framework for Random Sample Consensus
Rahul Raguram, Ondrej Chum, Marc Pollefeys, Jiri Matas and Jan-Michael Frahm.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012.
In this work, 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 algorithm that implements the USAC framework by leveraging state of the art techniques for the various modules – this then can be used by researchers either as a stand-alone tool for robust estimation, or as a benchmark for evaluating new techniques.