full
search.png

Search

close.png

Cancel

arrow
Volume 26, Issue 2
Surface Reconstruction of 3D Scattered Data with Radial Basis Functions

Xinwei Du, Xiaoying Yang & Xuezhang Liang

Commun. Math. Res., 26 (2010), pp. 183-192.

Published online: 2021-05

Preview Purchase PDF 1197 21415

Cited by

google scholar semantic scholar
Export citation
  • Abstract

We use Radial Basis Functions (RBFs) to reconstruct smooth surfaces from 3D scattered data. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. We propose improvements on the methods of surface reconstruction with radial basis functions. A sparse approximation set of scattered data is constructed by reducing the number of interpolating points on the surface. We present an adaptive method for finding the off-surface normal points. The order of the equation decreases greatly as the number of the off-surface constraints reduces gradually. Experimental results are provided to illustrate that the proposed method is robust and may draw beautiful graphics.

  • Keywords

radial basis function, scattered data, implicit surface, surface reconstruction.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{CMR-26-183, author = {Du , XinweiYang , Xiaoying and Liang , Xuezhang}, title = {Surface Reconstruction of 3D Scattered Data with Radial Basis Functions}, journal = {Communications in Mathematical Research }, year = {2021}, volume = {26}, number = {2}, pages = {183--192}, abstract = {

We use Radial Basis Functions (RBFs) to reconstruct smooth surfaces from 3D scattered data. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. We propose improvements on the methods of surface reconstruction with radial basis functions. A sparse approximation set of scattered data is constructed by reducing the number of interpolating points on the surface. We present an adaptive method for finding the off-surface normal points. The order of the equation decreases greatly as the number of the off-surface constraints reduces gradually. Experimental results are provided to illustrate that the proposed method is robust and may draw beautiful graphics.

}, issn = {2707-8523}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/cmr/19170.html} }
TY - JOUR T1 - Surface Reconstruction of 3D Scattered Data with Radial Basis Functions AU - Du , Xinwei AU - Yang , Xiaoying AU - Liang , Xuezhang JO - Communications in Mathematical Research VL - 2 SP - 183 EP - 192 PY - 2021 DA - 2021/05 SN - 26 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/cmr/19170.html KW - radial basis function, scattered data, implicit surface, surface reconstruction. AB -

We use Radial Basis Functions (RBFs) to reconstruct smooth surfaces from 3D scattered data. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. We propose improvements on the methods of surface reconstruction with radial basis functions. A sparse approximation set of scattered data is constructed by reducing the number of interpolating points on the surface. We present an adaptive method for finding the off-surface normal points. The order of the equation decreases greatly as the number of the off-surface constraints reduces gradually. Experimental results are provided to illustrate that the proposed method is robust and may draw beautiful graphics.

XinweiDu, XiaoyingYang & XuezhangLiang. (2021). Surface Reconstruction of 3D Scattered Data with Radial Basis Functions. Communications in Mathematical Research . 26 (2). 183-192. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard