# Fast and Robust Iterative Closest Point

arXiv: https://arxiv.org/abs/2007.07627 · HTML: https://mlsanity.com/paper/2007.07627/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 30 of 67)

- [GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator](https://mlsanity.com/paper/2102.12145/) (2021)
- [Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera](https://mlsanity.com/paper/2206.15258/) (2022)
- [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://mlsanity.com/paper/2306.16329/) (2023)
- [SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2310.17359/) (2023)
- [DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation](https://mlsanity.com/paper/2311.17084/) (2023)
- [SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs](https://mlsanity.com/paper/2309.12188/) (2023)
- [GaussReg: Fast 3D Registration with Gaussian Splatting](https://mlsanity.com/paper/2407.05254/) (2024)
- [SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction](https://mlsanity.com/paper/2309.10748/) (2023)
- [Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization](https://mlsanity.com/paper/2206.03410/) (2022)
- [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024)
- [PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering](https://mlsanity.com/paper/2209.00798/) (2022)
- [KSS-ICP: Point Cloud Registration based on Kendall Shape Space](https://mlsanity.com/paper/2211.02807/) (2022)
- [Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis](https://mlsanity.com/paper/2406.18817/) (2024)
- [CMHANet: A Cross-Modal Hybrid Attention Network for Point Cloud Registration](https://mlsanity.com/paper/2603.12721/) (2026)
- [A Robust Loss for Point Cloud Registration](https://mlsanity.com/paper/2108.11682/) (2021)
- [A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse](https://mlsanity.com/paper/2108.02800/) (2021)
- [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)
- [Video Perception Models for 3D Scene Synthesis](https://mlsanity.com/paper/2506.20601/) (2025)
- [MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies](https://mlsanity.com/paper/2409.15517/) (2024)
- [EasyREG: Easy Depth-Based Markerless Registration and Tracking using Augmented Reality Device for Surgical Guidance](https://mlsanity.com/paper/2504.09498/) (2025)
- [SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention](https://mlsanity.com/paper/2308.03718/) (2023)
- [4DRecons: 4D Neural Implicit Deformable Objects Reconstruction from a single RGB-D Camera with Geometrical and Topological Regularizations](https://mlsanity.com/paper/2406.10167/) (2024)
- [A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds](https://mlsanity.com/paper/2202.06857/) (2022)
- [Predictive World Models from Real-World Partial Observations](https://mlsanity.com/paper/2301.04783/) (2023)
- [GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing](https://mlsanity.com/paper/2302.01109/) (2023)
- [DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid](https://mlsanity.com/paper/2311.08159/) (2023)
- [A Riemannian Approach for Spatiotemporal Analysis and Generation of 4D Tree-shaped Structures](https://mlsanity.com/paper/2408.12443/) (2024)
- [TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery](https://mlsanity.com/paper/2212.14131/) (2022)
- [Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior](https://mlsanity.com/paper/2311.01432/) (2023)
- [An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation](https://mlsanity.com/paper/2403.06317/) (2024)

## Abstract (arXiv)

The Iterative Closest Point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.