English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Sparse Surface Reconstruction with Adaptive Partition of Unity and Radial Basis Functions

Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2006). Sparse Surface Reconstruction with Adaptive Partition of Unity and Radial Basis Functions. Graphical Models, 68(1), 15-24. doi:10.1016/j.gmod.2005.08.001.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Ohtake, Yutaka1, Author           
Belyaev, Alexander1, Author           
Seidel, Hans-Peter1, Author                 
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

Content

show
hide
Free keywords: -
 Abstract: A new implicit surface fitting method for surface reconstruction
from scattered point data is proposed. The method combines an
adaptive partition of unity approximation with least-squares RBF
fitting and is capable of generating a high quality surface
reconstruction. Given a set of points scattered over a smooth surface,
first a sparse set of overlapped local approximations is constructed.
The partition of unity generated from these local
approximants already gives a faithful surface reconstruction.
The final reconstruction is obtained by adding compactly supported
RBFs. The main feature of the developed approach consists of
using various regularization schemes which lead to economical,
yet accurate surface reconstruction.

Details

show
hide
Language(s): eng - English
 Dates: 2007-03-092006
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 314459
Other: Local-ID: C125675300671F7B-BD82841960CEFE13C12570F8004B68E9-Ohtake-gmod06a
BibTex Citekey: Ohtake-et-al_GM06
DOI: 10.1016/j.gmod.2005.08.001
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Graphical Models
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: San Diego, Calif. : Academic Press
Pages: - Volume / Issue: 68 (1) Sequence Number: - Start / End Page: 15 - 24 Identifier: ISSN: 1524-0703
CoNE: https://pure.mpg.de/cone/journals/resource/954922651186