The comparison of region growing algorithms with using EMST for point clouds

Close region interference is a problem in point cloud processing at the selection of local region step. Thus, growing local region with discarding the unwanted points is a crucial stage before post processing. Euclidean minimum spanning tree (EMST) is widely used to overcome this problem. This study aims to investigate the utilization of EMST for three commonly used region growing algorithms k-nearest neighbor (k-NN), circular region growing (CRG) and hybrid connection table (HCT). The experiments are conducted on two different data sets that one represents close region interference while the other has varying thickness problems. The results are presented quantitatively by measuring similarity of the reconstructed curve with the original one and the algorithm runtime. Visual results are also presented. EMST usage is improving the accuracy of local region at close region interference while increasing algorithm run time. Consequently, HCT with or without EMST is the most preferable method.