# KSS-ICP: Point Cloud Registration based on Kendall Shape Space

arXiv: https://arxiv.org/abs/2211.02807 · HTML: https://mlsanity.com/paper/2211.02807/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [RPM-Net: Robust Point Matching using Learned Features](https://mlsanity.com/paper/2003.13479/) (2020)
- [PRNet: Self-Supervised Learning for Partial-to-Partial Registration](https://mlsanity.com/paper/1910.12240/) (2019)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [Fast and Robust Iterative Closest Point](https://mlsanity.com/paper/2007.07627/) (2020)
- [PCRNet: Point Cloud Registration Network using PointNet Encoding](https://mlsanity.com/paper/1908.07906/) (2019)
- [Weakly Supervised 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2007.13970/) (2020)
- [L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention](https://mlsanity.com/paper/1908.00720/) (2019)
- [LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation](https://mlsanity.com/paper/2009.00164/) (2020)
- [Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement](https://mlsanity.com/paper/1903.04630/) (2019)

## Built on by (most cited first, 10 of 10)

- [PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark](https://mlsanity.com/paper/2408.05500/) (2024)
- [Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization](https://mlsanity.com/paper/2401.06975/) (2024)
- [EasyREG: Easy Depth-Based Markerless Registration and Tracking using Augmented Reality Device for Surgical Guidance](https://mlsanity.com/paper/2504.09498/) (2025)
- [FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space](https://mlsanity.com/paper/2312.03325/) (2023)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [Registration of 3D Point Sets Using Exponential-based Similarity Matrix](https://mlsanity.com/paper/2505.04540/) (2025)
- [PKSS-Align: Robust Point Cloud Registration on Pre-Kendall Shape Space](https://mlsanity.com/paper/2508.04286/) (2025)
- [iMatcher: Improve matching in point cloud registration via local-to-global geometric consistency learning](https://mlsanity.com/paper/2509.08982/) (2025)
- [TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration](https://mlsanity.com/paper/2601.18168/) (2026)
- [Structured Analytic Coherent Point Drift for Non-Rigid Point Set Registration](https://mlsanity.com/paper/2605.00934/) (2026)

## Abstract (arXiv)

Point cloud registration is a popular topic which has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose a new registration method, KSS-ICP, to address the rigid registration task in Kendall shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient space that removes influences of translations, scales, and rotations for shape feature-based analysis. Such influences can be concluded as the similarity transformations that do not change the shape feature. The point cloud representation in KSS is invariant to similarity transformations. We utilize such property to design the KSS-ICP for point cloud registration. To tackle the difficulty to achieve the KSS representation in general, the proposed KSS-ICP formulates a practical solution that does not require complex feature analysis, data training, and optimization. With a simple implementation, KSS-ICP achieves more accurate registration from point clouds. It is robust to similarity transformation, non-uniform density, noise, and defective parts. Experiments show that KSS-ICP has better performance than the state of the art.