PKSS-Align: Robust Point Cloud Registration on Pre-Kendall Shape Space

TL;DR

PKSS-Align introduces a shape-based point cloud registration method on Pre-Kendall shape space that is robust to transformations, noise, and incomplete data, without needing training or complex features.

Contribution

The paper proposes a novel registration approach using shape measurement on PKSS, improving robustness and efficiency without training or complex feature encoding.

Findings

Outperforms state-of-the-art methods in robustness and accuracy

Handles various transformations, noise, and incomplete data effectively

Achieves significant efficiency improvements with parallel acceleration

Abstract

Point cloud registration is a classical topic in the field of 3D Vision and Computer Graphics. Generally, the implementation of registration is typically sensitive to similarity transformations (translation, scaling, and rotation), noisy points, and incomplete geometric structures. Especially, the non-uniform scales and defective parts of point clouds increase probability of struck local optima in registration task. In this paper, we propose a robust point cloud registration PKSS-Align that can handle various influences, including similarity transformations, non-uniform densities, random noisy points, and defective parts. The proposed method measures shape feature-based similarity between point clouds on the Pre-Kendall shape space (PKSS), \textcolor{black}{which is a shape measurement-based scheme and doesn't require point-to-point or point-to-plane metric.} The employed measurement can be regarded as the manifold metric that is robust to various representations in the Euclidean coordinate system. Benefited from the measurement, the transformation matrix can be directly generated for point clouds with mentioned influences at the same time. The proposed method does not require data training and complex feature encoding. Based on a simple parallel acceleration, it can achieve significant improvement for efficiency and feasibility in practice. Experiments demonstrate that our method outperforms the relevant state-of-the-art methods.