mlsanityData to 14 Oct 2026

VSAC: Efficient and Accurate Estimator for H and F

arXiv 2106.10240 ↗First posted 18 Jun 2021cs.CVcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 8 of its 38 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

8 of 38 references.

References · 8

  1. 2017HPatches: A benchmark and evaluation of handcrafted and learned local descriptorscited by 264
  2. 2019MAGSAC++, a fast, reliable and accurate robust estimatorcited by 161
  3. 2017Graph-Cut RANSACcited by 133
  4. 2019Neural-Guided RANSAC: Learning Where to Sample Model Hypothesescited by 129
  5. 2018MAGSAC: marginalizing sample consensuscited by 123
  6. 2015MODS: Fast and Robust Method for Two-View Matchingcited by 26
  7. 2019Progressive NAPSAC: sampling from gradually growing neighborhoodscited by 16
  8. 2021USACv20: robust essential, fundamental and homography matrix estimationcited by 1

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 8; 2024: 1; 2025: 3.

17 papers.

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