# Guaranteed Outlier Removal for Point Cloud Registration with Correspondences

arXiv: https://arxiv.org/abs/1711.10209 · HTML: https://mlsanity.com/paper/1711.10209/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 30 of 62)

- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [3D Registration with Maximal Cliques](https://mlsanity.com/paper/2305.10854/) (2023)
- [nerf2nerf: Pairwise Registration of Neural Radiance Fields](https://mlsanity.com/paper/2211.01600/) (2022)
- [Self-supervised Geometric Perception](https://mlsanity.com/paper/2103.03114/) (2021)
- [Fast and Robust Registration of Partially Overlapping Point Clouds](https://mlsanity.com/paper/2112.09922/) (2021)
- [Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization](https://mlsanity.com/paper/2109.03349/) (2021)
- [RANSIC: Fast and Highly Robust Estimation for Rotation Search and Point Cloud Registration using Invariant Compatibility](https://mlsanity.com/paper/2104.09133/) (2021)
- [FastMAC: Stochastic Spectral Sampling of Correspondence Graph](https://mlsanity.com/paper/2403.08770/) (2024)
- [Scalable 3D Registration via Truncated Entry-wise Absolute Residuals](https://mlsanity.com/paper/2404.00915/) (2024)
- [Optimal Pose and Shape Estimation for Category-level 3D Object Perception](https://mlsanity.com/paper/2104.08383/) (2021)
- [ARCS: Accurate Rotation and Correspondence Search](https://mlsanity.com/paper/2203.14493/) (2022)
- [Accelerating Globally Optimal Consensus Maximization in Geometric Vision](https://mlsanity.com/paper/2304.05156/) (2023)
- [Multiway Point Cloud Mosaicking with Diffusion and Global Optimization](https://mlsanity.com/paper/2404.00429/) (2024)
- [Advances in Global Solvers for 3D Vision](https://mlsanity.com/paper/2602.14662/) (2026)
- [TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios](https://mlsanity.com/paper/2111.00657/) (2021)
- [Estimation Contracts for Outlier-Robust Geometric Perception](https://mlsanity.com/paper/2208.10521/) (2022)
- [G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model](https://mlsanity.com/paper/2308.11573/) (2023)
- [A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration](https://mlsanity.com/paper/2205.07404/) (2022)
- [Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search](https://mlsanity.com/paper/2404.06155/) (2024)
- [Multi-instance Point Cloud Registration by Efficient Correspondence Clustering](https://mlsanity.com/paper/2111.14582/) (2021)
- [Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints](https://mlsanity.com/paper/2206.12498/) (2022)
- [GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection](https://mlsanity.com/paper/2211.00207/) (2022)
- [Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms](https://mlsanity.com/paper/2302.07184/) (2023)
- [PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration](https://mlsanity.com/paper/2209.00219/) (2022)
- [DANIEL: A Fast and Robust Consensus Maximization Method for Point Cloud Registration with High Outlier Ratios](https://mlsanity.com/paper/2110.05075/) (2021)
- [Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences](https://mlsanity.com/paper/2305.11716/) (2023)
- [CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration](https://mlsanity.com/paper/2402.15464/) (2024)
- [IRON: Invariant-based Highly Robust Point Cloud Registration](https://mlsanity.com/paper/2103.04357/) (2021)
- [Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization](https://mlsanity.com/paper/2111.04228/) (2021)

## Abstract (arXiv)

An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust techniques to require very high computational cost. In this paper, we present a novel preprocessing method called guaranteed outlier removal for point cloud registration. Our method reduces the input to a smaller set, in a way that any rejected correspondence is guaranteed to not exist in the globally optimal solution. The reduction is performed using purely geometric operations which are deterministic and fast. Our method significantly reduces the population of outliers, such that further optimization can be performed quickly. Further, since only true outliers are removed, the globally optimal solution is preserved. On various synthetic and real data experiments, we demonstrate the effectiveness of our preprocessing method. Demo code is available as supplementary material.