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

TL;DR

KSS-ICP is a novel point cloud registration method that leverages Kendall shape space to achieve accurate, robust alignment invariant to transformations, without complex feature analysis or training.

Contribution

The paper introduces KSS-ICP, a simple yet effective registration method using Kendall shape space that improves accuracy and robustness over existing techniques.

Findings

Outperforms state-of-the-art registration methods

Robust to noise, density variations, and partial data

Achieves higher registration accuracy

Abstract

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.