A Learning-Free Noise-Adaptive Framework for Feature-Preserving Point Cloud Denoising

Point cloud denoising is a fundamental preprocessing task in 3D vision and geometry processing, where the main challenge is to suppress corruption while preserving sharp features, thin structures, and local surface fidelity. Classical geometric filters are computationally efficient and interpretable...

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Bibliographic Details

Main Authors:	Artur Janowski, Ahmet Emin Karkınlı, Mustafa Hüsrevoğlu, Talha Taşkanat, Abdüsselam Kesikoğlu
Format:	Artigo
Language:	Inglês
Published:	MDPI AG 2026-07-01
Series:	Applied Sciences
Subjects:	point cloud denoising feature-preserving filtering bilateral normal refinement normal projection local noise estimation learning-free geometry processing
Online Access:	https://www.mdpi.com/2076-3417/16/13/6635
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