Implicit surface reconstruction based on a new interpolation / approximation radial basis function

2021 ◽  
pp. 102062
Author(s):  
Yajun Zeng ◽  
Yuanpeng Zhu
Keyword(s):  
Radial Basis Function ◽  
Surface Reconstruction ◽  
Basis Function ◽  
Implicit Surface ◽  
Radial Basis

Hole Filling Algorithm in Surface Reconstruction Based on Radial Basis Function Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 750-754
Author(s):  
X.M. Wu ◽  
Gui Xian Li ◽  
W.M. Zhao
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Radial Basis Function ◽  
Surface Reconstruction ◽  
Basis Function ◽  
Hole Filling ◽  
Cloud Data ◽  
Learning Speed ◽  
Radial Basis ◽  
Data Surface

Aiming at hole filling in points cloud data reconstruction, a novel neural network arithmetic was employed in abridged points cloud data surface reconstruction. Radial basis function neural network and simulated annealing arithmetic was combined. Global optimization feature of simulated annealing was employed to adjust the network weights, the arithmetic can keep the network from getting into local minimum. MATLAB program was compiled, experiments on abridged points cloud data have been done employing this arithmetic, the result shows that this arithmetic can efficiently approach the surface with 10-4 mm error precision, and also the learning speed is quick and hole filling algorithm is successful and the reconstruction surface is smooth. Different methods have been employed to do surface reconstruction in comparison, the results illustrate the error employed algorithmic proposed in the paper is little and converge speed is quick.

A FAST METHOD FOR IMPLICIT SURFACE RECONSTRUCTION BASED ON RADIAL BASIS FUNCTIONS NETWORK FROM 3D SCATTERED POINTS

2007 ◽  
Vol 17 (06) ◽  
pp. 459-465 ◽  
Author(s):  
HANBO LIU ◽  
XIN WANG ◽  
WENYI QIANG
Keyword(s):  
Uniform Distribution ◽  
Radial Basis Functions ◽  
Surface Reconstruction ◽  
Basis Function ◽  
Basis Functions ◽  
Surface Model ◽  
Fast Method ◽  
Feature Points ◽  
Implicit Surface ◽  
Radial Basis

A method for arbitrary surface reconstruction from 3D large scattered points is proposed in this paper. According to the properties of 3D points, e.g. the non-uniform distribution and unknown topology, an implicit surface model is adopted based on radial basis functions network. And because of the property of locality of radial basis function, the method is fast and robust in surface reconstruction. Furthermore, an adapted K-Means algorithm is used to reduce reconstruction centers. For features completeness, two effective methods are introduced to extract the feature points before the adapted K-Means algorithm. Experiment results show that the presented approach is a good solution for reconstruction from 3D large scattered points.

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