NeFSAC: Neurally Filtered Minimal Samples
نویسندگان
چکیده
AbstractSince RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering motion-inconsistent poorly-conditioned samples. We train NeFSAC predict probability sample leading accurate relative pose, based on pixel coordinates image correspondences. Our learns typical motion patterns which lead unstable poses, regularities in possible motions favour well-conditioned likely-correct The novel lightweight architecture implements main invariants pose estimation, training scheme addresses problem extreme class imbalance. can be plugged into any existing RANSAC-based pipeline. integrate it USAC show that consistently provides strong speed-ups even under train-test domain gaps – example, trained autonomous driving scenario works PhotoTourism too. tested more than 100 k pairs from three publicly available real-world datasets found leads one order magnitude speed-up, while finding results alone. source code is https://github.com/cavalli1234/NeFSAC.KeywordsRANSACEpipolar geometry estimationMinimal samplesMachine learningMotion priorAutonomous
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-19824-3_21