Paper
23 February 2023 Point cloud data repair based on linear maximum entropy algorithm
Xiangping Jia, Tianwei Chen, Peng Liu, Jin Liu
Author Affiliations +
Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125512F (2023) https://doi.org/10.1117/12.2668311
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China
Abstract
Aiming at the problem of missing data after point cloud data filtering, this paper proposes a point cloud repair method based on the principle of Linear Maximum Entropy, which converts the Maximum Entropy Model into a linear model, and then uses linear programming to solve the model. The mathematical expectation and variance in the elevation value is used to establish linear constraints, and the weight coefficient of the sampling point is solved by the maximum entropy value to determine the elevation value of the fixed point, so as to complete the point cloud repair. By comparing with Maximum Entropy Method and Inverse Distance Weight method, the feasibility of Linear Maximum Entropy Model in point cloud data repairs is discussed. The results show that the point cloud data repaired by the Linear Maximum Entropy Model is more accurate, and a high-quality model can be established.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiangping Jia, Tianwei Chen, Peng Liu, and Jin Liu "Point cloud data repair based on linear maximum entropy algorithm", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125512F (23 February 2023); https://doi.org/10.1117/12.2668311
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KEYWORDS
Point clouds

Data modeling

3D modeling

Matrices

Analytical research

Data conversion

Mathematical modeling

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