Point cloud data repair based on linear maximum entropy algorithm

Xiangping Jia; Tianwei Chen; Peng Liu; Jin Liu

doi:10.1117/12.2668311

23 February 2023 Point cloud data repair based on linear maximum entropy algorithm

Xiangping Jia, Tianwei Chen, Peng Liu, Jin Liu

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.

Citation Download Citation

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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