Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor

Abstract

Automatic registration of unordered point clouds collected by the terrestrial laser scanner (TLS) is the prerequisite for many applications including 3D model reconstruction, cultural heritage management, forest structure assessment, landslide monitoring, and solar energy analysis. However, most of the existing point cloud registration methods still suffer from some limitations. On one hand, most of them are considerable time-consuming and high computational complexity due to the exhaustive pairwise search for recovering the underlying overlaps, which makes them infeasible for the registration of large-scale point clouds. On the other hand, most of them only leverage pairwise overlaps and rarely use the overlaps between multiple point clouds, resulting in difficulty dealing with point clouds with limited overlaps. To overcome these limitations, this paper presents a Hierarchical Merging based Multiview Registration (HMMR) algorithm to align unordered point clouds from various scenes. First, the multi-level descriptors (i.e., local descriptor: Binary Shape Context (BSC) and global descriptor: Vector of Locally Aggregated Descriptor (VLAD)) are calculated. Second, the point clouds overlapping (adjacent) graph is efficiently constructed by leveraging the similarity between their corresponding VLAD vectors. Finally, the proposed method hierarchically registers multiple point clouds by iteratively performing optimal registration point clouds calculation, BSC descriptor based pairwise registration and point cloud groups overlapping (adjacent) graph update, until all the point clouds are aligned into a common coordinate reference. Comprehensive experiments demonstrate that the proposed algorithm obtains good performance in terms of successful registration rate, rotation error, translation error, and runtime, and outperformed the state-of-the-art approaches.