DocumentCode :
3062882
Title :
Robust Smooth Feature Extraction from Point Clouds
Author :
Daniels, Joel ; Ha, Linh K. ; Ochotta, Tilo ; Silva, Claudio T.
Author_Institution :
Univ. of Utah, Salt Lake City
fYear :
2007
fDate :
13-15 June 2007
Firstpage :
123
Lastpage :
136
Abstract :
Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of robust moving least squares to locally fit surfaces to potential features. Using Newton´s method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression.
Keywords :
Newton method; feature extraction; least squares approximations; solid modelling; 3D model; Newton method; multistep refinement method; point cloud; point-based geometry compression; robust moving least square; robust smooth feature extraction; Clouds; Feature extraction; Filtering; Least squares methods; Reverse engineering; Robustness; Solid modeling; Surface fitting; Surface reconstruction; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Shape Modeling and Applications, 2007. SMI '07. IEEE International Conference on
Conference_Location :
Lyon
Print_ISBN :
0-7695-2815-5
Type :
conf
DOI :
10.1109/SMI.2007.32
Filename :
4273375
Link To Document :
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