Paper Info
Title
Consolidation of low-quality point clouds from outdoor scenes
Abstract
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.
Year
DOI
Venue
2013
10.1111/cgf.12187
Comput. Graph. Forum
Keywords
Field
DocType
low-quality point data,novel approach,noise smoothing,point cloud,outdoor scene,noisy point,outlier removal method,clustering method,fast approach,point data,low-quality point cloud
Computer vision,Anomaly detection,Computer science,Communication channel,Outlier,Filter (signal processing),Lidar,Smoothing,Artificial intelligence,Cluster analysis,Point cloud
Journal
Volume
Issue
ISSN
32
5
0167-7055
Citations 
PageRank 
References 
19
0.78
24
Authors
7
Name
Order
Citations
PageRank
Jun Wang137247.52
Kai Xu293450.68
Ligang Liu31960108.77
Junjie Cao421218.07
Shengjun Liu511613.79
Zeyun Yu628027.13
Xianfeng Gu72997189.71