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

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

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

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.

scholarly journals Fast Feature-Preserving Approach to Carpal Bone Surface Denoising

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2379
Author(s):  
Ibrahim Salim ◽  
A. Hamza
Keyword(s):  
Objective Function ◽  
Linear Equations ◽  
Noise Model ◽  
Carpal Bone ◽  
Geometric Framework ◽  
Sparse System ◽  
Adaptive Kernel ◽  
Feature Preserving ◽  
Data Adaptive ◽  
Matrix Balancing

We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.

Feature preserving multiresolution subdivision and simplification of point clouds: A conformal geometric algebra approach

2017 ◽  
Vol 41 (11) ◽  
pp. 4074-4087 ◽  
Author(s):  
Shuai Yuan ◽  
Shuai Zhu ◽  
Dong-Shuang Li ◽  
Wen Luo ◽  
Zhao-Yuan Yu ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Point Clouds ◽  
Conformal Geometric Algebra ◽  
Algebra Approach ◽  
Feature Preserving
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