Registration for cross-source point clouds based on Gaussian mixture model

Guangyu Wu; Mingfeng Li; Wenlai Ji; Ding Tan; Weida Fang; Xiwei Li

doi:10.1117/12.3019512

23 January 2024 Registration for cross-source point clouds based on Gaussian mixture model

Guangyu Wu, Mingfeng Li, Wenlai Ji, Ding Tan, Weida Fang, Xiwei Li

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129782O (2024) https://doi.org/10.1117/12.3019512
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

To solve the incomplete point cloud model obtained by a single instrument, cross-source point clouds are widely used. When data are taken from various surveying instruments, the problems, such as density variation and viewpoint variation, are caused. A registration method based on Gaussian Mixture Model is proposed in this study. With the threedimensional information of the point clouds described by the model, the registration of two kinds of point clouds is transformed into the probability density estimation. A water tower is chosen as the example, and the difference between both the algorithm based on the Gaussian Mixture Model and the Iterative Closest Point algorithm are compared. The results show that the former method behaves better than the latter in both runtime and accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangyu Wu, Mingfeng Li, Wenlai Ji, Ding Tan, Weida Fang, and Xiwei Li "Registration for cross-source point clouds based on Gaussian mixture model", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129782O (23 January 2024); https://doi.org/10.1117/12.3019512

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