A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
This addresses the challenge of aligning 3D point clouds from different sensors for applications like mapping or robotics, though it appears incremental as it builds on existing registration techniques.
The paper tackles the problem of registering cross-source point clouds with large variations, such as missing data and density differences, by proposing a systematic method that extracts macro and micro structures, resulting in successful registration where other methods fail.
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.