MAGSAC++, a Fast, Reliable and Accurate Robust Estimator
| Autor: | Daniel Barath, Maksym Ivashechkin, Jana Noskova, Jiri Matas |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Robust statistics Sampling (statistics) Estimator 020207 software engineering QA75 Electronic computers. Computer science / számítástechnika számítógéptudomány 02 engineering and technology Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Geometric modeling business Algorithm |
| Zdroj: | CVPR |
| Popis: | A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not require the inlier-outlier decision, and a novel marginalization procedure formulated as an iteratively re-weighted least-squares approach. We also propose a new sampler, Progressive NAPSAC, for RANSAC-like robust estimators. Exploiting the fact that nearby points often originate from the same model in real-world data, it finds local structures earlier than global samplers. The progressive transition from local to global sampling does not suffer from the weaknesses of purely localized samplers. On six publicly available real-world datasets for homography and fundamental matrix fitting, MAGSAC++ produces results superior to state-of-the-art robust methods. It is faster, more geometrically accurate and fails less often. arXiv admin note: substantial text overlap with arXiv:1906.02295 |
| Databáze: | OpenAIRE |
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