Rigid blocks matching method based on contour curves and feature regions

Abstract

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

Research Progress in the Splicing and Restoration of Artifact Fragments Based on Point Cloud

“…etc (Zhao F.Q., 2022) used the KD-tree algorithm to obtain the nearest corresponding point of the matching point pair during the iterative process and then used the Euclidean Distance of the corresponding point as a constraint. Zhao, F.Q., etc (Zhao, F.Q., 2018) proposed the AIF-ICP, which adds a dynamic iteration factor to the iterative process of the ICP algorithm to improve the computational efficiency. But the ICP algorithm cannot be applied to the case where the two adjacency fragments have no overlap region due to the breakage of the artifact fragments, resulting in certain limitations.…”