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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 toapproximate 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.


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Towards uniform point distribution in feature-preserving point cloud filtering

Show Author's information Shuaijun Chen1,*Jinxi Wang2,*Wei Pan3Shang Gao1Meili Wang2( )Xuequan Lu1( )
School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
College of Information Engineering, Northwest A&F University, Yangling 712100, China
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China

*Shuaijun Chen and Jinxi Wang contributed equally to this work.

Abstract

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 toapproximate 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.

Keywords:

point cloud filtering, point distribution, feature preservation
Received: 10 January 2022 Accepted: 20 February 2022 Published: 03 January 2023 Issue date: June 2023
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Received: 10 January 2022
Accepted: 20 February 2022
Published: 03 January 2023
Issue date: June 2023

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