Paper Info
Title
A new 3D model retrieval approach based on the elevation descriptor
Abstract
The advances in 3D data acquisition techniques, graphics hardware, and 3D data modeling and visualizing techniques have led to the proliferation of 3D models. This has made the searching for specific 3D models a vital issue. Techniques for effective and efficient content-based retrieval of 3D models have therefore become an essential research topic. In this paper, a novel feature, called elevation descriptor, is proposed for 3D model retrieval. The elevation descriptor is invariant to translation and scaling of 3D models and it is robust for rotation. First, six elevations are obtained to describe the altitude information of a 3D model from six different views. Each elevation is represented by a gray-level image which is decomposed into several concentric circles. The elevation descriptor is obtained by taking the difference between the altitude sums of two successive concentric circles. An efficient similarity matching method is used to find the best match for an input model. Experimental results show that the proposed method is superior to other descriptors, including spherical harmonics, the MPEG-7 3D shape spectrum descriptor, and D2.
Year
DOI
Venue
2007
10.1016/j.patcog.2006.04.034
Pattern Recognition
Keywords
Field
DocType
efficient content-based retrieval,elevation descriptor,altitude information,concentric circle,model retrieval,altitude sum,model retrieval approach,input model,shape spectrum descriptor,data acquisition technique,efficient similarity,graphics hardware,data acquisition,spectrum,data model,spherical harmonic
Similitude,Data modeling,Concentric,Graphics hardware,Pattern recognition,Data acquisition,Spherical harmonics,Invariant (mathematics),Artificial intelligence,Elevation,Mathematics
Journal
Volume
Issue
ISSN
40
1
Pattern Recognition
Citations 
PageRank 
References 
64
1.50
18
Authors
3
Name
Order
Citations
PageRank
Jau-Ling Shih121711.64
Chang-Hsing Lee243931.86
Jian Tang Wang3641.50