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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Veröffentlicht in:Computer graphics forum Jg. 32; H. 5; S. 207 - 216
Hauptverfasser: Wang, Jun, Xu, Kai, Liu, Ligang, Cao, Junjie, Liu, Shengjun, Yu, Zeyun, Gu, Xianfeng David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.08.2013
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ark:/67375/WNG-HCXJ2R40-Z
ArticleID:CGF12187
istex:8CE4DE806766D7A47EAECA3746A59677BFE92131
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ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12187