Feature Preserving Octree‐Based Hexahedral Meshing

2019 ◽  
Vol 38 (5) ◽  
pp. 135-149 ◽  
Author(s):  
Xifeng Gao ◽  
Hanxiao Shen ◽  
Daniele Panozzo
Keyword(s):  
Hexahedral Meshing ◽  
Feature Preserving

Feature Preserving Mesh Simplification Algorithm Based on Square Volume Measure

2009 ◽  
Vol 32 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Yuan-Feng ZHOU ◽  
Cai-Ming ZHANG ◽  
Ping HE
Keyword(s):  
Mesh Simplification ◽  
Volume Measure ◽  
Feature Preserving

A feature-preserving volumetric technique to merge surface triangulations

2002 ◽  
Vol 55 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Juan R. Cebral ◽  
Fernando E. Camelli ◽  
Rainald Löhner
Keyword(s):  
Feature Preserving

scholarly journals Feature-preserving interpolation and filtering of environmental time series

2015 ◽  
Vol 72 ◽  
pp. 71-76 ◽  
Author(s):  
Gregoire Mariethoz ◽  
Niklas Linde ◽  
Damien Jougnot ◽  
Hassan Rezaee
Keyword(s):  
Time Series ◽  
Feature Preserving ◽  
Environmental Time Series

scholarly journals Hexahedral Meshing With Varying Element Sizes

2017 ◽  
Vol 36 (8) ◽  
pp. 540-553 ◽  
Author(s):  
Kaoji Xu ◽  
Xifeng Gao ◽  
Zhigang Deng ◽  
Guoning Chen
Keyword(s):  
Hexahedral Meshing

Simplification algorithm of denture point cloud based on feature preserving

2021 ◽  
pp. 1-14
Author(s):  
Shigang Wang ◽  
Shuai Peng ◽  
Jiawen He
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Characteristic Point ◽  
Cloud Model ◽  
Average Distance ◽  
Normal Vector ◽  
Sampling Point ◽  
Characteristic Region ◽  
K Value ◽  
Feature Preserving

Due to the point cloud of oral scan denture has a large amount of data and redundant points. A point cloud simplification algorithm based on feature preserving is proposed to solve the problem that the feature preserving is incomplete when processing point cloud data and cavities occur in relatively flat regions. Firstly, the algorithm uses kd-tree to construct the point cloud spatial topological to search the k-Neighborhood of the sampling point. On the basis of that to calculate the curvature of each point, the angle between the normal vector, the distance from the point to the neighborhood centroid, as well as the standard deviation and the average distance from the point to the neighborhood on this basis, therefore, the detailed features of point cloud can be extracted by multi-feature extraction and threshold determination. For the non-characteristic region, the non-characteristic point cloud is spatially divided through Octree to obtain the K-value of K-means clustering algorithm and the initial clustering center point. The simplified results of non-characteristic regions are obtained after further subdivision. Finally, the extracted detail features and the reduced result of non-featured region will be merged to obtain the final simplification result. The experimental results show that the algorithm can retain the characteristic information of point cloud model better, and effectively avoid the phenomenon of holes in the simplification process. The simplified results have better smoothness, simplicity and precision, and are of high practical value.

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