Paper
8 May 2023 An improved feature point selection algorithm for point cloud data
Xuedong Jing, Xueqi Shan, Yuwei Zhang
Author Affiliations +
Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350E (2023) https://doi.org/10.1117/12.2679106
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China
Abstract
At present, curve and surface fitting is widely used in three-dimensional measurement, industrial design, archaeology, medicine and other fields, and curve and surface fitting has also become a hot spot and a difficulty at present. The surface point cloud data scanned by high-precision 3D laser scanning instruments on site are often complex, and the data are relatively dense for curves. If the approximation fitting is used, complex information may not be reflected enough, and the interpolation fitting may produce over-fitting phenomenon. This paper proposes a feature point selection algorithm, which is more targeted for dense point cloud data than the general cubic B-spline interpolation algorithm. The feature point selection algorithm can retain feature points and remove non-feature points and minimize the number of fitting segments on the premise of meeting the accuracy requirements of the final fitting curve.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xuedong Jing, Xueqi Shan, and Yuwei Zhang "An improved feature point selection algorithm for point cloud data", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350E (8 May 2023); https://doi.org/10.1117/12.2679106
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Point clouds

3D modeling

Reflection

Data modeling

Interpolation

Reverse modeling

Back to Top