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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Detaylı Bibliyografya
Asıl Yazarlar:	Artur Janowski, Ahmet Emin Karkınlı, Mustafa Hüsrevoğlu, Talha Taşkanat, Abdüsselam Kesikoğlu
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	MDPI AG 2026-07-01
Seri Bilgileri:	Applied Sciences
Konular:	point cloud denoising feature-preserving filtering bilateral normal refinement normal projection local noise estimation learning-free geometry processing
Online Erişim:	https://www.mdpi.com/2076-3417/16/13/6635
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

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

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