Point cloud simplification for the boundary preservation based on extracted four features |
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| Affiliation: | 1. School of Medical Technology, Guangdong Medical University, Dongguan, China;2. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;3. Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China;4. Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China;5. School of Biomedical Engineering, Guangdong Medical University, Dongguan, China;6. Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen 518045, China;1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;3. Schoolof Computer Scienceand Technology,Civil Aviation University of China, Tianjin 300300, China;1. Department of Oral Surgery, Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, and Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, Shanghai 200011, China;2. Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai 201100, China;3. Department of medical record statistics, Ningbo City First Hospital, Ningbo Hospital of Zhejiang University, Ningbo 315010, China |
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| Abstract: | This paper proposes a simplification algorithm based on four feature parameters, aiming at solving the problem that the edge features cannot be retained due to the incompletely extracted sharp features during point cloud simplification. Firstly, K neighborhood searching is carried out for point cloud, and K neighborhood points are quickly found by a dynamic grid method. Then, four features including: the curvature of the point, the average of the normal angle of a point from a neighborhood point, the average distance between the point and the neighborhood point and the distance between the point and the center of gravity of the neighborhood point, are calculated according to the K neighborhood of the data point. The four parameters are used to define the feature discrimination parameters and feature thresholds, to compare the size and extract the feature points; finally, the non-feature points are reduced twice by the method of the bounding box, and the reduced point cloud and feature points are spliced to achieve the purpose of simplification. The experimental results show that the distance between the point and the center of gravity of the neighborhood has a great influence on the simplified model boundary, which effectively guarantees the accuracy of the simplified model. |
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| Keywords: | Point cloud simplification K-neighborhood searching Feature extraction Boundary preservation |
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