Paper Info
Title
Extracting lines of curvature from noisy point clouds
Abstract
We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.
Year
DOI
Venue
2009
10.1016/j.cad.2008.12.004
Computer-Aided Design
Keywords
Field
DocType
extracting line,robust statistical estimate,curvature information,input point cloud,noisy point cloud,robust framework,robust curvature estimation,novel approach,sharp surface feature,point cloud,point cloud denoising,real-world input datasets,lines of curvature,quad mesh construction,outlier rejection,point cloud shape analysis,shape analysis,energy minimization,robust statistics
Noise reduction,Mathematical optimization,Curvature,Segmentation,Outlier,Point cloud,Normal,Mathematics,Energy minimization,Shape analysis (digital geometry)
Journal
Volume
Issue
ISSN
41
4
Computer-Aided Design
Citations 
PageRank 
References 
23
0.92
42
Authors
4
Name
Order
Citations
PageRank
Evangelos Kalogerakis1137753.82
Derek Nowrouzezahrai280154.49
Patricio Simari31558.26
Karan Singh4152976.00