Paper Info
Title
3D Model Retrieval Based on 2D Slice Similarity Measurements
Abstract
In this paper, we present an approach based on 2D slices for measuring similarity between 3D models. The key idea is to represent the 3D model by a series of slices along certain directions so that the shape-matching problem between 3D models is transformed into similarity measuring between 2D slices. Here, we have to deal with the following problems: selection of cutting directions, cutting methods, and similarity measuring. To solve these problems, some strategies and rules are proposed. Firstly, a maximum normal distribution method is presented to get three ortho-axes that coincide better with human visual perception mechanism. Secondly, a cutting method is given which can be used to get a series of slices composed of a set of closed polygons. Thirdly, on the basis of 3D shape distribution method presented by Robert et al., we develop a 2D shape distribution method to measure the similarity between the 2D slices. Some experiments are given in this paper to show the validity of this method for 3D model retrieval.
Year
DOI
Venue
2004
10.1109/3DPVT.2004.3
3DPVT
Keywords
Field
DocType
computational geometry,image segmentation,visual perception,image retrieval,normal distribution
Polygon,Normal distribution,Pattern recognition,Human visual perception,Image matching,Computational geometry,Image retrieval,Image segmentation,Artificial intelligence,Visual perception,Mathematics
Conference
ISBN
Citations 
PageRank 
0-7695-2223-8
18
1.02
References 
Authors
10
6
Name
Order
Citations
PageRank
Jiantao Pu127723.12
Yi Liu211622.97
Guyu Xin3242.01
Hongbin Zha42206183.36
Weibin Liu57119.97
Yusuke Uehara6628.15