USAC: A Universal Framework for Random Sample Consensus

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 som...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 35; číslo 8; s. 2022 - 2038
Hlavní autoři: Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Los Alamitos, CA IEEE 01.08.2013
IEEE Computer Society
Témata:
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial intelligence
Computational modeling
Computer science; control theory; systems
Context
Data models
Estimation
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
RANSAC
robust estimation
Robustness
Software
Software engineering
Theoretical computing
Computer vision
Parameter estimation
Program library
Estimator robustness
Activity
C language
Contamination
Standards
Outlier
RANSAC
Software libraries
Model matching
Efficiency
robust estimation
Data models
Signal to noise ratio
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2012.257