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Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity.pdf (591.27 kB)

Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

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posted on 16.12.2020, 05:11 by Sorush Niknamian
Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

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Email Address of Submitting Author

so.niknamian@gmail.com

ORCID of Submitting Author

0000-0002-2385-8590

Submitting Author's Institution

Liberty University

Submitting Author's Country

United States of America

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Exports