# AA-ICP: Iterative Closest Point with Anderson Acceleration

arXiv: https://arxiv.org/abs/1709.05479 · HTML: https://mlsanity.com/paper/1709.05479/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2017.

## Built on by (most cited first, 17 of 17)

- [Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization](https://mlsanity.com/paper/2206.03410/) (2022)
- [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)
- [LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud](https://mlsanity.com/paper/2206.05927/) (2022)
- [Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism](https://mlsanity.com/paper/2205.02996/) (2022)
- [DeepBBS: Deep Best Buddies for Point Cloud Registration](https://mlsanity.com/paper/2110.03016/) (2021)
- [DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling](https://mlsanity.com/paper/2310.11733/) (2023)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [RegHEC: Hand-Eye Calibration via Simultaneous Multi-view Point Clouds Registration of Arbitrary Object](https://mlsanity.com/paper/2304.14092/) (2023)
- [Large-scale DSM registration via motion averaging](https://mlsanity.com/paper/2405.19442/) (2024)
- [EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera](https://mlsanity.com/paper/2409.19305/) (2024)
- [Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation](https://mlsanity.com/paper/2506.18922/) (2025)
- [Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration](https://mlsanity.com/paper/2211.16301/) (2022)
- [Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy](https://mlsanity.com/paper/2410.21857/) (2024)
- [DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration](https://mlsanity.com/paper/2508.17034/) (2025)
- [GMOR: A Lightweight Robust Point Cloud Registration Framework via Geometric Maximum Overlapping](https://mlsanity.com/paper/2508.17427/) (2025)
- [Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets](https://mlsanity.com/paper/2604.26318/) (2026)

## Abstract (arXiv)

Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust method, it is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into Point Cloud Library (PCL) and make it available online. Benchmarking on real-world data fully supports our claims.