# Less Biased Noise Scale Estimation for Threshold-Robust RANSAC

arXiv: https://arxiv.org/abs/2503.13433 · HTML: https://mlsanity.com/paper/2503.13433/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [DKM: Dense Kernelized Feature Matching for Geometry Estimation](https://mlsanity.com/paper/2202.00667/) (2022)
- [ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer](https://mlsanity.com/paper/2208.14201/) (2022)
- [MAGSAC++, a fast, reliable and accurate robust estimator](https://mlsanity.com/paper/1912.05909/) (2019)
- [MAGSAC: marginalizing sample consensus](https://mlsanity.com/paper/1803.07469/) (2018)
- [Revisiting Sampson Approximations for Geometric Estimation Problems](https://mlsanity.com/paper/2401.07114/) (2024)
- [Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods](https://mlsanity.com/paper/2302.06949/) (2023)

## Built on by (most cited first, 4 of 4)

- [RANSAC Scoring Functions: Analysis and Reality Check](https://mlsanity.com/paper/2512.19850/) (2025)
- [Are Pretrained Image Matchers Good Enough for SAR-Optical Satellite Registration?](https://mlsanity.com/paper/2604.10217/) (2026)
- [Global Truncated Loss Minimization for Robust and Threshold-Resilient Geometric Estimation](https://mlsanity.com/paper/2603.14796/) (2026)
- [Robust Local Optimization Done Right](https://mlsanity.com/paper/2610.05743/) (2026)

## Abstract (arXiv)

The gold-standard for robustly estimating relative pose through image matching is RANSAC. While RANSAC is powerful, it requires setting the inlier threshold that determines whether the error of a correspondence under an estimated model is sufficiently small to be included in its consensus set. Setting this threshold is typically done by hand, and is difficult to tune without an access to ground truth data. Thus, a method capable of automatically determining the optimal threshold would be desirable. In this paper we revisit inlier noise scale estimation, which is an attractive approach as the inlier noise scale is linear to the optimal threshold. We revisit the noise scale estimation method SIMFIT and find bias in the estimate of the noise scale. In particular, we fix underestimates from using the same data for fitting the model as estimating the inlier noise, and from not taking the threshold itself into account. Secondly, since the optimal threshold within a scene is approximately constant we propose a multi-pair extension of SIMFIT++, by filtering of estimates, which improves results. Our approach yields robust performance across a range of thresholds, shown in Figure 1. Code is available at https://github.com/Parskatt/simfitpp