swMATH ID: 43918
Software Authors: Daniel Barath, Jana Noskova, Maksym Ivashechkin, Jiri Matas
Description: MAGSAC++, a fast, reliable and accurate robust estimator. 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.
Homepage: https://arxiv.org/abs/1912.05909
Source Code:  https://github.com/danini/magsac
Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV
Related Software: KITTI; MLESAC; MNIST; BRISK; PointNet; SOSNet; L2-Net; Theia; R2D2; SuperGlue; ORB-SLAM2; ORB-SLAM; D2-Net; ContextDesc; ASLFeat; SuperPoint; FlowNet; MonoSLAM; HPatches; Key.Net
Cited in: 4 Documents

Citations by Year