3D point cloud simplification based on the clustering algorithm and introducing the Shannon’s entropy

Abdelaaziz Mahdaoui; El Hassan Sbai

doi:10.1117/12.2588384

4 January 2021 3D point cloud simplification based on the clustering algorithm and introducing the Shannon’s entropy

Abdelaaziz Mahdaoui, El Hassan Sbai

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050N (2021) https://doi.org/10.1117/12.2588384
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

In the field of 3D digitization of real objects using modern scanning devices, dense point clouds can be obtained. This data point can have redundancy. To solve this problem, we present a new simplification method based on clustering and Shannon's entropy. This approach optimizes the number of 3D point clouds by keeping the original point cloud characteristics. To show the robustness of the technique, we have applied it on different point cloud and making comparisons with other methods. It can be said, according to the obtained results, that our method is effective.

Citation Download Citation

Abdelaaziz Mahdaoui and El Hassan Sbai "3D point cloud simplification based on the clustering algorithm and introducing the Shannon’s entropy", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050N (4 January 2021); https://doi.org/10.1117/12.2588384

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