Marginalizing Sample Consensus.
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| Titel: | Marginalizing Sample Consensus. |
|---|---|
| Autoren: | Barath, Daniel1 dbarath@inf.ethz.ch, Noskova, Jana2 Jana.Noskova@cvut.cz, Matas, Jiri2 matas@cmp.felk.cvut.cz |
| Quelle: | IEEE Transactions on Pattern Analysis & Machine Intelligence. Nov2022, Vol. 44 Issue 11, p8420-8432. 13p. |
| Schlagwörter: | *C++, *DECISION making, *ALGORITHMS, SOURCE code, PYTHON programming language |
| Abstract: | A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac. [ABSTRACT FROM AUTHOR] |
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| Datenbank: | Business Source Index |
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