A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures

TL;DR

This paper introduces a robust, systematic method for cross-source point cloud registration that preserves macro and micro structures, effectively handling data variations and missing information, with demonstrated superior performance over existing methods.

Contribution

A novel registration pipeline that maintains macro and micro structures using graph-based methods, improving robustness in cross-source point cloud alignment.

Findings

Successfully registers cross-source point clouds with large variations.

Outperforms existing methods on cross-source datasets.

Effective in same-source datasets as well.

Abstract

We propose a systematic approach for registering cross-source point clouds. The compelling need for cross-source point cloud registration is motivated by the rapid development of a variety of 3D sensing techniques, but many existing registration methods face critical challenges as a result of the large variations in cross-source point clouds. This paper therefore illustrates a novel registration method which successfully aligns two cross-source point clouds in the presence of significant missing data, large variations in point density, scale difference and so on. The robustness of the method is attributed to the extraction of macro and micro structures. Our work has three main contributions: (1) a systematic pipeline to deal with cross-source point cloud registration; (2) a graph construction method to maintain macro and micro structures; (3) a new graph matching method is proposed which considers the global geometric constraint to robustly register these variable graphs. Compared to most of the related methods, the experiments show that the proposed method successfully registers in cross-source datasets, while other methods have difficulty achieving satisfactory results. The proposed method also shows great ability in same-source datasets.