Consolidation of Low-quality Point Clouds from Outdoor Scenes

The emergence of laser/LiDAR sensors, reliable multi‐view stereo techniques and more recently consumer depth cameras have brought point clouds to the forefront as a data format useful for a number of applications. Unfortunately, the point data from those channels often incur imperfection, frequently...

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Vydáno v:	Computer graphics forum Ročník 32; číslo 5; s. 207 - 216
Hlavní autoři:	Wang, Jun, Xu, Kai, Liu, Ligang, Cao, Junjie, Liu, Shengjun, Yu, Zeyun, Gu, Xianfeng David
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford, UK Blackwell Publishing Ltd 01.08.2013
Témata:	Algorithms Channels Computer graphics Computer science Consolidation Feature preserving Filtering I.3.3 [Computer Graphics]: 3D point data-Outlier detection I.3.3 [Computer Graphics]: 3D point data—Outlier detection; Normal estimation; Noise smoothing; Feature preserving Image processing systems Lasers Noise Noise smoothing Normal estimation Outdoor Sensors Smoothing Studies Three dimensional models United States > US
ISSN:	0167-7055, 1467-8659
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Popis
Shrnutí:	The emergence of laser/LiDAR sensors, reliable multi‐view stereo techniques and more recently consumer depth cameras have brought point clouds to the forefront as a data format useful for a number of applications. Unfortunately, the point data from those channels often incur imperfection, frequently contaminated with severe outliers and noise. This paper presents a robust consolidation algorithm for low‐quality point data from outdoor scenes, which essentially consists of two steps: 1) outliers filtering and 2) noise smoothing. We first design a connectivity‐based scheme to evaluate outlierness and thereby detect sparse outliers. Meanwhile, a clustering method is used to further remove small dense outliers. Both outlier removal methods are insensitive to the choice of the neighborhood size and the levels of outliers. Subsequently, we propose a novel approach to estimate normals for noisy points based on robust partial rankings, which is the basis of noise smoothing. Accordingly, a fast approach is exploited to smooth noise, while preserving sharp features. We evaluate the effectiveness of the proposed method on the point clouds from a variety of outdoor scenes.
Bibliografie:	ark:/67375/WNG-HCXJ2R40-Z ArticleID:CGF12187 istex:8CE4DE806766D7A47EAECA3746A59677BFE92131 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0167-7055 1467-8659
DOI:	10.1111/cgf.12187