A New Progressive Simplification Method for Point Cloud Using Local Entropy of Normal Angle

Xuan, Wei; Hua, Xian-Sheng; Chen, Xijiang; Zou, Juhong; He, Xingui

doi:10.1007/s12524-017-0730-6

Cited by 33 publications

(15 citation statements)

References 14 publications

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“…Many studies simplify point cloud based on normal vector. Xuan et al [17] used PCA to calculate the normal vector of each point, and then calculated normal angle local entropy to evaluate the importance of point. They deleted the least important point based on the importance and updated the normal vector to simplify the point cloud recursively.…”

Section: Calculating the Normal Vectormentioning

confidence: 99%

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A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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3D point cloud simplification is an important pretreatment in surface reconstruction for sparing computer resources and improving reconstruction speed. However, existing methods often sacrifice the simplification precision to improve the simplification speed, or sacrifice the speed to improve precision. A proper balance between the simplification speed and the simplification accuracy is still a challenge. In this paper, we propose a new simplification method based on the importance of point. Named as detail feature points simplified algorithm (DFPSA), this algorithm has distinct processes to achieve improvements in three aspects. First, a rule of k neighborhood search is set to ensure the points found are the closest to the sample point. In this way, the accuracy of calculated normal vector of the point cloud is significantly improved, and the search speed is largely increased. Second, a formula that considers multiple characteristics for measuring the importance of point is proposed. Thereupon, the main detail features of the point cloud are preserved. Finally, an octree structure is employed to simplify the remaining points, through which holes in reconstructing point cloud are obviously reduced. The DFPSA is applied to four different data sets, and the corresponding results are compared with those of other five algorithms. The experimental results demonstrate that the DFPSA brings better simplification effects than existing counterparts, and the DFPSA not only can simplify point cloud but also has good effect in simplifying subject's narrow contours.INDEX TERMS 3D geometric point cloud simplification, detail feature point, k neighborhood search rule, the importance of point.

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Section: Calculating the Normal Vectormentioning

confidence: 99%

“…There are also other direct simplification algorithms. Xuan et al [17] constructed normal angle local entropy to assess point, and simplified point cloud in a gradual way. Zang et al [18] presented a multi-level method for retaining geometric features of different sizes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

Liu

Fan

et al. 2019

IEEE Access

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“…Meanwhile, Xuan et al adopted the local entropy based on the normal angle to evaluate the importance of points, which is derived on the basis of the normal angle and information entropy theory through the estimation of the normal vector. The point cloud is finally simplified by removing the least important points, which are evaluated by gradually updating the normal vector and the corresponding importance value [28].…”

Section: Reduction Methods Based On Geometric Featuresmentioning

confidence: 99%

High-Precision 3D Reconstruction for Small-to-Medium-Sized Objects Utilizing Line-Structured Light Scanning: A Review

2021

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Three-dimensional reconstruction technology has demonstrated broad application potential in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last few years. High-quality digital point cloud information exists to help researchers to understand objects and environments. However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements based on technical principles. Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details, is lacking. This paper takes the technological path as the logical sequence to provide a detailed summary of the latest development status of each key technology, which will serve potential users and new researchers in this field. The focus is placed on exploring studies reconstructing small-to-medium-sized objects, as opposed to performing large-scale reconstructions in the field.

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“…However, its initialization cost is high, and it requires the maintenance of an overall priority queue, which is a disadvantage for large samples of points. Xuan et al [9] proposed a progressive point cloud simplification technique, founded on the theory of the information entropy and normal angle. e fundamental of this technique is to find the importance of points using the information entropy of the normal angle.…”

Section: Introductionmentioning

confidence: 99%

“…is work is based on this concept to select the set of points grouped into cluster in order to simplify the original point cloud. Information theory is presented in different areas such as data processing [19,20], data clustering [21], and 3D point cloud simplification [1,9].…”

Section: Introductionmentioning

confidence: 99%

3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

Mahdaoui

Sbai

2020

Advances in Multimedia

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While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

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A New Progressive Simplification Method for Point Cloud Using Local Entropy of Normal Angle

Cited by 33 publications

References 14 publications

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

High-Precision 3D Reconstruction for Small-to-Medium-Sized Objects Utilizing Line-Structured Light Scanning: A Review

3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

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