Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models

Kun Zhang,Shiquan Qiao,Xiaohong Wang,Yongqiang Zhang,Yongtao Yang

doi:10.3390/app9102130

Kun Zhang, Shiquan Qiao + Show 3 more

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https://doi.org/10.3390/app9102130

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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 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.

Highlights

With the development of 3D scanning technology, a number of portable devices have appeared, and the application of 3D graphics has widened, for example, Microsoft’s Kinect, Hololens, and Intel’sRealSense are used in VR, reverse engineering, non-contact measurement, and so on
Where R is the data set of the point cloud, which is the full set of data, R = C ∪ S; H is the set of point cloud simplification entropy; PCMM is the set of point cloud matching models, which is composed of natural quadric shape models; C is the data set that is reduced by FPPS; S is the data set that is saved by FPPS, which is C’s complementary set; and rules represent the mapping rules, which are set in terms of the PCMM
This paper proposed a point cloud data simplification method, FPPS, which can reduce the point cloud volume and protect the geometric features hidden in the point cloud

Summary

Introduction

With the development of 3D scanning technology, a number of portable devices have appeared, and the application of 3D graphics has widened, for example, Microsoft’s Kinect, Hololens, and Intel’sRealSense are used in VR (virtual reality), reverse engineering, non-contact measurement, and so on. With the development of 3D scanning technology, a number of portable devices have appeared, and the application of 3D graphics has widened, for example, Microsoft’s Kinect, Hololens, and Intel’s. Taking Kinect (V2) as an example, data can be collected at a rate of 12 MB per second. Dense point clouds produce a huge volume of data. The massive data volume produces unimaginable pressure for 3D processing, such as the reverse reconstruction of objects. In order to simplify the data volume, many algorithms have been provided so far. Among these algorithms, point cloud simplification by voxelization is the most widely used method, especially in reverse engineering [2,3,4].

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