Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity.pdf (591.27 kB)
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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.
History
Email Address of Submitting Author
so.niknamian@gmail.comORCID of Submitting Author
0000-0002-2385-8590Submitting Author's Institution
Liberty UniversitySubmitting Author's Country
- United States of America