Complicated Point Cloud Model Segmentation Based on Multi-view Region Growing
KONG De-ming1,ZHANG Na1,WANG Shu-tao1,SHI Hui-chao2
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Abstract：In order to improve the segmentation accuracy of the 3D point cloud model in the feature fuzzy region, a segmentation method based on multi-view region growing was proposed.Based on the principle of direction difference of normal vectors of grids, the model was divided into different categories of sub-regions.Then the one-to-one mapping relationships between point cloud and multi-view distance images were established in the corresponding regions.The sensitivity of Canny operator for gray level was used to obtain independent connected domains and their barycentric coordinates were calculated. The corresponding points were extracted as seed points in 3D point cloud.To separate the adjacent surfaces, the offset angle of normal vectors of grids was introduced.At the same time, the remaining independent surfaces were extracted according to the principle of iterative nearest points.To achieve segmentation optimization, KNN algorithm was used to remove the off-group points.Experiments were carried out on the selected model data set.The results showed that the complicated point cloud model could be divided reasonably by the proposed method, and the segmentation accuracy was not less than 80%.
孔德明,张娜,王书涛,史慧超. 基于多视角区域生长的复杂点云模型分割[J]. 计量学报, 2021, 42(6): 704-709.
KONG De-ming,ZHANG Na,WANG Shu-tao,SHI Hui-chao. Complicated Point Cloud Model Segmentation Based on Multi-view Region Growing. Acta Metrologica Sinica, 2021, 42(6): 704-709.
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