# Space-Partitioning RANSAC

arXiv: https://arxiv.org/abs/2111.12385 · HTML: https://mlsanity.com/paper/2111.12385/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [Learning to Find Good Correspondences](https://mlsanity.com/paper/1711.05971/) (2017)
- [MAGSAC++, a fast, reliable and accurate robust estimator](https://mlsanity.com/paper/1912.05909/) (2019)
- [Learning Two-View Correspondences and Geometry Using Order-Aware Network](https://mlsanity.com/paper/1908.04964/) (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)
- [Progressive Correspondence Pruning by Consensus Learning](https://mlsanity.com/paper/2101.00591/) (2021)
- [Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm](https://mlsanity.com/paper/1906.02290/) (2019)
- [MODS: Fast and Robust Method for Two-View Matching](https://mlsanity.com/paper/1503.02619/) (2015)
- [VSAC: Efficient and Accurate Estimator for H and F](https://mlsanity.com/paper/2106.10240/) (2021)
- [Efficient Initial Pose-graph Generation for Global SfM](https://mlsanity.com/paper/2011.11986/) (2020)
- [Deterministic Approximate Methods for Maximum Consensus Robust Fitting](https://mlsanity.com/paper/1710.10003/) (2017)
- [Consensus Maximization Tree Search Revisited](https://mlsanity.com/paper/1908.02021/) (2019)
- [Minimal Solutions for Panoramic Stitching Given Gravity Prior](https://mlsanity.com/paper/2012.00465/) (2020)

## Built on by (most cited first, 6 of 6)

- [SupeRANSAC: One RANSAC to Rule Them All](https://mlsanity.com/paper/2506.04803/) (2025)
- [Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information](https://mlsanity.com/paper/2409.11536/) (2024)
- [AffineGlue: Joint Matching and Robust Estimation](https://mlsanity.com/paper/2307.15381/) (2023)
- [Progressive Correspondence Regenerator for Robust 3D Registration](https://mlsanity.com/paper/2502.02163/) (2025)
- [Dense Match Summarization for Faster Two-view Estimation](https://mlsanity.com/paper/2506.02893/) (2025)
- [Fixing the RANSAC Stopping Criterion](https://mlsanity.com/paper/2503.07829/) (2025)

## Abstract (arXiv)

A new algorithm is proposed to accelerate RANSAC model quality calculations. The method is based on partitioning the joint correspondence space, e.g., 2D-2D point correspondences, into a pair of regular grids. The grid cells are mapped by minimal sample models, estimated within RANSAC, to reject correspondences that are inconsistent with the model parameters early. The proposed technique is general. It works with arbitrary transformations even if a point is mapped to a point set, e.g., as a fundamental matrix maps to epipolar lines. The method is tested on thousands of image pairs from publicly available datasets on fundamental and essential matrix, homography and radially distorted homography estimation. On average, it reduces the RANSAC run-time by 41% with provably no deterioration in the accuracy. It can be straightforwardly plugged into state-of-the-art RANSAC frameworks, e.g. VSAC.