Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Markovic, Veljko; Jakovljević, Živana; Miljković, Zoran

doi:10.17559/tv-20180328175336

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…The third type of method is to measure the structure and composition information of the point cloud from a global perspective. Markovic et al proposed a simplified method for the sensitization of 3D point cloud features based on ε-insensitive support vector regression, which is suitable for structured point clouds [33]. The algorithm uses the flatness characteristics of the ε-support vector regression machine to effectively identify points in the high-curvature area, which are saved in a simplified point cloud along with a reduced number of points from a flat area.…”

Section: Reduction Methods Based On Component Analysismentioning

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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Section: Reduction Methods Based On Component Analysismentioning

confidence: 99%

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

2021

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“…Xuan et al [23] evaluate the importance of points based on the local entropy of normal angles of points. Markovic et al [24] propose a method based on insensitive support vector regression. It can identify areas with different curvatures and reserve them to varying degrees.…”

Section: Introductionmentioning

confidence: 99%

A novel point cloud simplification method using local conditional information

Cheng¹,

Li²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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In three-dimensional measurement of large-scale parts, such as car bodies and aircraft wings, massive points (up to hundreds of millions) are collected. If all point cloud data is processed, a large amount of computing resources and storage space will be consumed. It is an important task to reduce the burden of processing while maintaining the feature of the point cloud as much as possible. This paper proposes a novel point cloud simplification method using local conditional information that is utilized to evaluate the features of each point in point clouds. The proposed method can reduce the size of point clouds while reserving significant features. Firstly, the original information of each point is evaluated by calculating the curvature of the original point cloud. Secondly, the point with the maximal information is selected and reserved in the target point cloud that will be updated as the alternative point cloud after simplification. The conditional information of each rest point is evaluated by the neighborhood and updated to the information of the current point, which attenuates with the distance from the other reserved points. Finally, the point with the maximal information is reserved iteratively until the number of points reserved satisfies the requirements of simplification according to enterprise needs. Experiments verify the effectiveness and performance of the proposed method. The experimental results indicate that the proposed method performs much better on feature retention than Voxel simplification and Curvature simplification.

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“…Li et al [ 11 ] used this measure to simplify point clouds. Markovic et al [ 12 ] proposed a sensitive feature based on insensitive support vector regression.…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors

Shi

Meng

2022

Sensors

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Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.

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Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Cited by 10 publications

References 31 publications

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

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

A novel point cloud simplification method using local conditional information

A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors

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