Robust normal estimation in unstructured 3D point clouds by selective normal space exploration

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The ke...

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Vydané v:	The Visual computer Ročník 34; číslo 6-8; s. 961 - 971
Hlavní autori:	Mura, Claudio, Wyss, Gregory, Pajarola, Renato
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018 Springer Nature B.V
Predmet:	Artificial Intelligence Computer Graphics Computer Science Estimation Image Processing and Computer Vision Methods Neighborhoods Original Article Outliers (statistics) Robustness Space exploration Three dimensional models Robust statistics Normal estimation Point cloud processing
ISSN:	0178-2789, 1432-2315
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