Rigid block matching is the process of searching a 3D rigid transformation that can make the common parts of two surfaces of different blocks in different coordinates match correctly. This process has been widely used in many research fields

such as archaeology

biological engineering

and remote sensing data processing. A new matching method is proposed in this study to further improve the accuracy

convergence speed

and anti-noise capacity of the existing rigid block matching algorithms. The method can be divided into two steps

namely

coarse and fine matching processes. First

fracture surfaces are extracted from rigid blocks

and the concave and convex salient regions on fracture surfaces are calculated. Block coarse matching is completed through the matching algorithm based on salient regions. Second

the Gaussian probability model

angle constraint

and dynamic iteration coefficient are added to the iterative closest point (ICP) algorithm to improve ICP performance

and the improved ICP algorithm is used to further match the rigid blocks for fine matching of rigid blocks. In the experiment

two types of data models (public blocks and Terracotta Warrior blocks) are used to illustrate the performance of the improved ICP algorithm. Matching results show that the accuracy and convergence speed are increased by 50% and 65%

respectively

compared with the ICP algorithm and 15% and 50%

respectively

compared with the Picky ICP algorithm. The improved ICP algorithm is indeed a much more accurate

faster

and better anti-noise algorithm than other algorithms. This improved algorithm can match not only public blocks perfectly but also the special Terracotta Warrior blocks much better than other algorithms. This algorithm is a good method of rigid block matching with extensive applications.