# An Evaluation of Feature Matchers for Fundamental Matrix Estimation

arXiv: https://arxiv.org/abs/1908.09474 · HTML: https://mlsanity.com/paper/1908.09474/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [Learning to Match Features with Seeded Graph Matching Network](https://mlsanity.com/paper/2108.08771/) (2021)
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- [Adaptive Reordering Sampler with Neurally Guided MAGSAC](https://mlsanity.com/paper/2111.14093/) (2021)
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- [Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network](https://mlsanity.com/paper/2307.01447/) (2023)
- [Robust Fitting on a Gate Quantum Computer](https://mlsanity.com/paper/2409.02006/) (2024)
- [AMatFormer: Efficient Feature Matching via Anchor Matching Transformer](https://mlsanity.com/paper/2305.19205/) (2023)
- [USACv20: robust essential, fundamental and homography matrix estimation](https://mlsanity.com/paper/2104.05044/) (2021)
- [ResMatch: Residual Attention Learning for Local Feature Matching](https://mlsanity.com/paper/2307.05180/) (2023)
- [EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy](https://mlsanity.com/paper/2504.17280/) (2025)
- [CyberLoc: Towards Accurate Long-term Visual Localization](https://mlsanity.com/paper/2301.02403/) (2023)
- [FLIGHT: Fibonacci Lattice-based Inference for Geometric Heading in real-Time](https://mlsanity.com/paper/2602.23115/) (2026)
- [NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry](https://mlsanity.com/paper/2609.06074/) (2026)

## Abstract (arXiv)

Matching two images while estimating their relative geometry is a key step in many computer vision applications. For decades, a well-established pipeline, consisting of SIFT, RANSAC, and 8-point algorithm, has been used for this task. Recently, many new approaches were proposed and shown to outperform previous alternatives on standard benchmarks, including the learned features, correspondence pruning algorithms, and robust estimators. However, whether it is beneficial to incorporate them into the classic pipeline is less-investigated. To this end, we are interested in i) evaluating the performance of these recent algorithms in the context of image matching and epipolar geometry estimation, and ii) leveraging them to design more practical registration systems. The experiments are conducted in four large-scale datasets using strictly defined evaluation metrics, and the promising results provide insight into which algorithms suit which scenarios. According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator. They show remarkable performances and have potentials to a large part of computer vision tasks. To facilitate future research, the full evaluation pipeline and the proposed methods are made publicly available.