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Keywords = natural quadric shape models

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27 pages, 10985 KiB  
Article
Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
by Kun Zhang, Shiquan Qiao, Xiaohong Wang, Yongtao Yang and Yongqiang Zhang
Appl. Sci. 2019, 9(10), 2130; https://doi.org/10.3390/app9102130 - 24 May 2019
Cited by 39 | Viewed by 4840
Abstract
With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The [...] Read more.
With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise. Full article
(This article belongs to the Special Issue LiDAR and Time-of-flight Imaging)
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