VSAC: Efficient and Accurate Estimator for H and F
Maksym Ivashechkin, Daniel Barath, Jirí Matas
Abstract
We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the efficacy of the dominant plane handling and, also, allows near error-free rejection of incorrect models, without false positives. The local optimization process and its application is improved so that it is run on average only once. Further technical improvements include adaptive sequential hypothesis verification and efficient model estimation via Gaussian elimination. Experiments on four standard datasets show that VSAC is significantly faster than all its predecessors and runs on average in 1-2 ms, on a CPU. It is two orders of magnitude faster and yet as precise as MAGSAC++, the currently most accurate estimator of two-view geometry. In the repeated runs on EVD, HPatches, PhotoTourism, and Kusvod2 datasets, it never failed.
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Install the CLIlune papers fulltext 0120e386-a316-45e4-9afe-b6132e3d9d8eCited by top-tier papers10
- Generalized Differentiable RANSACTong Wei, Yash Patel, Alexander Shekhovtsov, Jirí Matas et al.ICCV 2023 · 43 citations
- Learning to Find Good Models in RANSACDaniel Barath, Luca Cavalli, Marc PollefeysCVPR 2022 · 41 citations
- BANSAC: A dynamic BAyesian Network for adaptive SAmple ConsensusValter Piedade, Pedro MiraldoICCV 2023 · 16 citations
- ARCS: Accurate Rotation and Correspondence SearchLiangzu Peng, Manolis C. Tsakiris, René VidalCVPR 2022 · 15 citations
- Adaptive Reordering Sampler with Neurally Guided MAGSACTong Wei, Jirí Matas, Daniel BarathICCV 2023 · 10 citations
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