# A Benchmark for Point Clouds Registration Algorithms

arXiv: https://arxiv.org/abs/2003.12841 · HTML: https://mlsanity.com/paper/2003.12841/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning](https://mlsanity.com/paper/2103.14533/) (2021)
- [Riedones3D: a celtic coin dataset for registration and fine-grained clustering](https://mlsanity.com/paper/2109.15033/) (2021)
- [Stress-Testing Point Cloud Registration on Automotive LiDAR](https://mlsanity.com/paper/2204.07719/) (2022)
- [End-to-End LiDAR optimization for 3D point cloud registration](https://mlsanity.com/paper/2602.10492/) (2026)
- [R3PM-Net: Real-time, Robust, Real-world Point Matching Network](https://mlsanity.com/paper/2604.05060/) (2026)

## Abstract (arXiv)

Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot be generalised. Public datasets proposed until now, taken individually, cover only a few kinds of environment and mostly a single sensor. For these reasons, we developed a benchmark, for localization and mapping applications, using multiple publicly available datasets. In this way, we are able to cover many kinds of environment and many kinds of sensor that can produce point clouds. Furthermore, the ground truth has been thoroughly inspected and evaluated to ensure its quality. For some of the datasets, the accuracy of the ground truth measuring system was not reported by the original authors, therefore we estimated it with our own novel method, based on an iterative registration algorithm. Along with the data, we provide a broad set of registration problems, chosen to cover different types of initial misalignment, various degrees of overlap, and different kinds of registration problems. Lastly, we propose a metric to measure the performances of registration algorithms: it combines the commonly used rotation and translation errors together, to allow an objective comparison of the alignments. This work aims at encouraging authors to use a public and shared benchmark, instead of data collected ad-hoc, to ensure objectivity and repeatability, two fundamental characteristics in any scientific field.