Towards uniform point distribution in feature-preserving point cloud filtering

Computational Visual Media

Research Article

Meili Wang¹  Jinxi Wang¹  Shuaijun Chen²  Shang Gao²  Xuequan Lu²  Wei Pan³ 

[1] College of Information Engineering, Northwest A&F University, 712100, Yangling, China;School of Information Technology, Deakin University, 3220, Geelong, VIC, Australia;School of Mechanical and Automotive Engineering, South China University of Technology, 510641, Guangzhou, China;

关键词: point cloud filtering;    point distribution;    feature preservation;   

DOI  :  10.1007/s41095-022-0278-4

 received in 2022-01-10, accepted in 2022-02-20,  发布年份 2022

来源: Springer

【 摘 要 】

While a popular representation of 3D data, point clouds may contain noise and need filtering before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term aims to approximate the noisy surfaces while preserving geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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