# VSAC: Efficient and Accurate Estimator for H and F

arXiv: https://arxiv.org/abs/2106.10240 · HTML: https://mlsanity.com/paper/2106.10240/

## 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 (most cited first, 8 of 8)

- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [MAGSAC++, a fast, reliable and accurate robust estimator](https://mlsanity.com/paper/1912.05909/) (2019)
- [Graph-Cut RANSAC](https://mlsanity.com/paper/1706.00984/) (2017)
- [Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://mlsanity.com/paper/1905.04132/) (2019)
- [MAGSAC: marginalizing sample consensus](https://mlsanity.com/paper/1803.07469/) (2018)
- [MODS: Fast and Robust Method for Two-View Matching](https://mlsanity.com/paper/1503.02619/) (2015)
- [Progressive NAPSAC: sampling from gradually growing neighborhoods](https://mlsanity.com/paper/1906.02295/) (2019)
- [USACv20: robust essential, fundamental and homography matrix estimation](https://mlsanity.com/paper/2104.05044/) (2021)

## Built on by (most cited first, 17 of 17)

- [Generalized Differentiable RANSAC](https://mlsanity.com/paper/2212.13185/) (2022)
- [ARCS: Accurate Rotation and Correspondence Search](https://mlsanity.com/paper/2203.14493/) (2022)
- [A Large Scale Homography Benchmark](https://mlsanity.com/paper/2302.09997/) (2023)
- [Two-View Geometry Scoring Without Correspondences](https://mlsanity.com/paper/2306.01596/) (2023)
- [NeFSAC: Neurally Filtered Minimal Samples](https://mlsanity.com/paper/2207.07872/) (2022)
- [SupeRANSAC: One RANSAC to Rule Them All](https://mlsanity.com/paper/2506.04803/) (2025)
- [Space-Partitioning RANSAC](https://mlsanity.com/paper/2111.12385/) (2021)
- [Handbook on Leveraging Lines for Two-View Relative Pose Estimation](https://mlsanity.com/paper/2309.16040/) (2023)
- [Adaptive Reordering Sampler with Neurally Guided MAGSAC](https://mlsanity.com/paper/2111.14093/) (2021)
- [BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus](https://mlsanity.com/paper/2309.08690/) (2023)
- [Robust Self-calibration of Focal Lengths from the Fundamental Matrix](https://mlsanity.com/paper/2311.16304/) (2023)
- [Image Matching Filtering and Refinement by Planes and Beyond](https://mlsanity.com/paper/2411.09484/) (2024)
- [RANSAC Scoring Functions: Analysis and Reality Check](https://mlsanity.com/paper/2512.19850/) (2025)
- [Consensus-Adaptive RANSAC](https://mlsanity.com/paper/2307.14030/) (2023)
- [Fixing the RANSAC Stopping Criterion](https://mlsanity.com/paper/2503.07829/) (2025)
- [Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation](https://mlsanity.com/paper/2305.17463/) (2023)
- [Intelligent Sampling Consensus for Homography Estimation in Football Videos Using Featureless Unpaired Points](https://mlsanity.com/paper/2310.04912/) (2023)

## 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.