# Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration

arXiv: https://arxiv.org/abs/1808.03899 · HTML: https://mlsanity.com/paper/1808.03899/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [GaussReg: Fast 3D Registration with Gaussian Splatting](https://mlsanity.com/paper/2407.05254/) (2024)
- [RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds](https://mlsanity.com/paper/2205.11028/) (2022)
- [Pairwise Point Cloud Registration using Graph Matching and Rotation-invariant Features](https://mlsanity.com/paper/2105.02151/) (2021)
- [GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds](https://mlsanity.com/paper/2604.20155/) (2026)
- [Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration](https://mlsanity.com/paper/2211.16301/) (2022)
- [Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems](https://mlsanity.com/paper/2501.18110/) (2025)
- [Iterative Differential Entropy Minimization (IDEM) method for fine rigid pairwise 3D Point Cloud Registration: A Focus on the Metric](https://mlsanity.com/paper/2601.09601/) (2026)

## Abstract (arXiv)

In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the globally optimal correspondences through optimizing the energy function by the Kuhn-Munkres algorithm and then calculate the transformation based on the correspondences. Finally,we iteratively refine the transformation between two point clouds by conducting optimal correspondences matching and transformation calculation in a mutually reinforcing manner, to achieve the coarse-to-fine registration under an unified framework.The proposed method is evaluated and compared to several state-of-the-art methods on selected challenging datasets with repetitive, symmetric and incomplete structures.Comprehensive experiments demonstrate that the proposed IGSP algorithm obtains good performance and outperforms the state-of-the-art methods in terms of both rotation and translation errors.