A discernible criterion for 3D point cloud based on multifractal spectrum

Kun Yu; Jie Ma; Bin Fang; Bingli Wu

doi:10.1117/12.2574409

24 June 2020 A discernible criterion for 3D point cloud based on multifractal spectrum

Kun Yu, Jie Ma, Bin Fang, Bingli Wu

Author Affiliations +

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 1152602 (2020) https://doi.org/10.1117/12.2574409
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

The traditional discernible criteria for a 2D target are mostly based on Johnson criterion, to overcome the limitations of the Johnson criterion and fill the gap in a 3D point cloud, a novel discernible criterion has been proposed for the 3D point cloud. Based on the multifractal spectrum, the spatial distribution of the 3D point cloud is described. By analyzing the multifractal spectra at different resolutions, feature trend and the final discernible resolution are concluded. The experimental results show that the limiting resolution of T90, F15C is 585mm, the limiting resolution of T90 and Rexton is 517mm, and the limiting resolution of F15C and Rexton is 541mm. The proposed discernible criteria can provide theoretical support for limit identification resolution of 3D point cloud target.

Citation Download Citation

Kun Yu, Jie Ma, Bin Fang, and Bingli Wu "A discernible criterion for 3D point cloud based on multifractal spectrum", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 1152602 (24 June 2020); https://doi.org/10.1117/12.2574409

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KEYWORDS

Clouds

3D modeling

Fractal analysis

3D acquisition

Data modeling

Statistical analysis

Target recognition

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