A 3D Object Retrieval System Based on Multi-Resolution Reeb Graph Ding-Yun Chen and Ming Ouhyoung Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan E-mail: {dynamic, ming}@cmlab.csie.ntu.edu.tw Abstract This paper proposes a 3D model retrieval system which extended the work of Hilaga in 2001. We apply the pre-processing stage to 3D models in practical use. The demo system with over 450 3D models from the Net is on the web page: http://3dsite.dhs.org/~dynamic, and can also be used in PocketPC with wireless LAN card. There are 445 various models in our database. 1. Introduction In this decade, multimedia data, which usually doesn’t need any text to represent, grow rapidly. Content-based retrieval for multimedia data becomes more and more important. In order to communicate with people’s information, the MPEG group aims to create MPEG-7 international standard, also known as “Multimedia Content Description Interface”, for the description of the multimedia data, including image, video, audio, 2D shape and 3D object [8]. 3D object retrieval research is active now, because the technique of 3D modeling and digitizing tools is on a progressive improvement. In the last few years, several articles have been devoted to the study of 3D object retrieval. Cyr and Kimia [5] proposed an aspect-graph approach. They generate a set of 2D silhouette for each 3D object, and then measure the similarity between two views by 2D shape similarity metrics. Kolonias et al. [3] proposed aspect ratio, a binary 3D shape mask and set of paths outlining the shape of the 3D object for matching. Paquet and Rioux [7] presented an approach for 3D models retrieval using the distribution of moment, normal, cord, color, material and texture. Zhang and Chen [2] propose a 3D model retrieval system using volume-surface ratio, aspect ratio, moment invariants and Fourier transformation coefficients. Elad et al. [6] apply relevant feedback to 3D object retrieval, which uses moments as features. Osada et al. [4] propose and analyze a method for computing shape signatures for arbitrary 3D polygonal models. Hilaga et al. [1] propose a technique in which similarity between polyhedral models is quickly, accurately, and automatically calculated by comparing the skeletal and topological structure. The structure decomposes 3D model to a one-dimensional graph structure. The graph is invariant to translation, rotation and scaling, robust against connectivity changes, and resistant against noise, certain changes due to deformation. Our system is based on the research of Hilaga, which is one of the best ideas among the previous works. Fig. 1 shows the flow chart of our system when querying by a 3D object. The last two stages are the same with Hilaga, please refer to [1]. Chapter 2 details the pre-processing stage in our system. 2. Pre-Processing of 3D Object This stage is designed for accurately and fast getting the search key. There are four steps in this stage: merging vertices, merging parts, re-sampling and adding short-cut edges. The first two steps solve practical problems when many models are used. The last two steps are modified from the approach of Hilaga [1].
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A 3D Object Retrieval System Based on Multi-Resolution Reeb Graph
Ding-Yun Chen and Ming Ouhyoung Department of Computer Science and Information Engineering,
National Taiwan University, Taipei, Taiwan E-mail: {dynamic, ming}@cmlab.csie.ntu.edu.tw
Abstract This paper proposes a 3D model retrieval
system which extended the work of Hilaga in 2001.
We apply the pre-processing stage to 3D models in
practical use. The demo system with over 450 3D
models from the Net is on the web page:
http://3dsite.dhs.org/~dynamic, and can also be used
in PocketPC with wireless LAN card. There are 445
various models in our database.
1. Introduction In this decade, multimedia data, which usually
doesn’t need any text to represent, grow rapidly.
Content-based retrieval for multimedia data becomes
more and more important. In order to communicate
with people’s information, the MPEG group aims to
create MPEG-7 international standard, also known as
“Multimedia Content Description Interface”, for the
description of the multimedia data, including image,
video, audio, 2D shape and 3D object [8]. 3D object
retrieval research is active now, because the
technique of 3D modeling and digitizing tools is on a
progressive improvement.
In the last few years, several articles have been
devoted to the study of 3D object retrieval. Cyr and
Kimia [5] proposed an aspect-graph approach. They
generate a set of 2D silhouette for each 3D object, and
then measure the similarity between two views by 2D
shape similarity metrics. Kolonias et al. [3] proposed
aspect ratio, a binary 3D shape mask and set of paths
outlining the shape of the 3D object for matching.
Paquet and Rioux [7] presented an approach for 3D
models retrieval using the distribution of moment,
normal, cord, color, material and texture. Zhang and
Chen [2] propose a 3D model retrieval system using
volume-surface ratio, aspect ratio, moment invariants
and Fourier transformation coefficients. Elad et al. [6]
apply relevant feedback to 3D object retrieval, which
uses moments as features. Osada et al. [4] propose and
analyze a method for computing shape signatures for
arbitrary 3D polygonal models. Hilaga et al. [1]
propose a technique in which similarity between
polyhedral models is quickly, accurately, and
automatically calculated by comparing the skeletal and
topological structure. The structure decomposes 3D
model to a one-dimensional graph structure. The graph
is invariant to translation, rotation and scaling, robust
against connectivity changes, and resistant against
noise, certain changes due to deformation.
Our system is based on the research of Hilaga,
which is one of the best ideas among the previous
works. Fig. 1 shows the flow chart of our system
when querying by a 3D object. The last two stages
are the same with Hilaga, please refer to [1]. Chapter
2 details the pre-processing stage in our system.
2. Pre-Processing of 3D Object This stage is designed for accurately and fast
getting the search key. There are four steps in this