Title :
The DSO Feature Based Point Cloud Simplification
Author :
Lee, Pai-Feng ; Huang, Chia-Ping
Author_Institution :
Dept. of Accounting Inf., Hsing-Wu Inst. of Technol., Taipei, Taiwan
Abstract :
This study proposes an effective low-error point cloud simplification method to retain the physical features of the models. The value of Discrete Shape Operator (DSO) is adopted to extract the features points of the models, and those are postponed to simplify. The value of DSO is defined as the discrete sum over the directional curvature and torsion. The proposed method improves the Quadric Error Metric of vertex pair contraction, it not only effectively simplifies the point cloud model and keeps the features of object model, but also decreases the preprocessing time cost associated with feature analysis. This study also proposes a method to obtain unique simplified model for each model and the time cost involved in calculating DSO is about 17.29% of the execution time. The unique simplified model obtained by this study can significantly reduce the computation cost about 72.72% than mesh simplification which reconstruct original points first.
Keywords :
feature extraction; mesh generation; solid modelling; DSO feature based point cloud simplification; discrete shape operator; feature analysis; feature extraction; low-error point cloud simplification method; mesh simplification; physical features; quadric error metric; vertex pair contraction; Computational modeling; Feature extraction; Mathematical model; Measurement; Shape; Surface reconstruction; Surface treatment; curvature; discrete shape operator; feature extraction; point simplification; torsion;
Conference_Titel :
Computer Graphics, Imaging and Visualization (CGIV), 2011 Eighth International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0981-4
DOI :
10.1109/CGIV.2011.26