CameraType Len
VII. C ONCLUSION AND F UTURE WORK
4. 不同三維模型述詞權重之調整與選擇
每種述詞有其特性與優缺點，很難只單用某一述詞而得到使用者所想要且滿意的結果。
如果提供使用者自由挑選及調整不同三維述詞權重的組合，便可以讓使用者更容易地得 到想要的三維模型。另一方面，如果事先研究並調配出三維模型述詞間合適的權重，結 合不同述詞間的優點與特性，讓使用者也可以更方便地得到他所想要的三維模型。
3-12
中。同時三維模型搜尋器找到的模型，
也可由工作台開啟。
有關三維 MPEG-4 動畫場景建構工作台(3D Workbench for MPEG-4 Scene and Animation Construction)：
預期工作項目 實際工作成果 說明
增強 MPEG-4 三維虛擬場景之力 回饋反應
使用了新的力學模型與計算方式，使得 使用者在操作的時候得到更佳的觸感
符合 三維模型後處理之技術 獨立實作出模型中軸自動產生、動畫三
維模型中軸驅動套用、具調節三維動畫 模型。
符合
傳 統 模 型 的 多 邊 形 資 料 (Polygonal Data)與工作站所用的 體積格資料(Volume Data)做轉換 用的介面需要定義與開發
已實作將 Maya OBJ 檔案與體積格資料 轉換的程式
符合
將三維 MPEG-4 動畫場景工作 台改寫成為三維建模動畫軟體 的外掛程式(Plug-ins)，如 Maya 軟體
單獨實作出 Maya plug-ins 可做出三 維模型中軸自動產生後處理。另一方面 將 Maya 場景物件與動畫場景建構工 作台做互相轉換。
符合
1. Chien-Chang Ho, Yan-Hong Lu, Hung-Te Lin, Shuen-Huei Guan, Sheng-Yao Cho, Rung-Huei Liang, Bing-Yu Chen, and Ming Ouhyoung, “Feature Refinement Strategy for Extended Marching Cubes: Handling on Dynamic Nature of Real-time Sculpting Application”, International Conference on Multimedia and Expo 2004, Taipei, Taiwan, May 2004.
2. Shuen-Huei Guan, Ming-Kei Hsieh, Chia-Chi Yeh and Bing-Yu Chen, “Enhanced 3D Model Retrieval System through Characteristic Views using Orthogonal Visual Hull”, SIGGRAPH 2004 (poster section), Los Angeles, USA, Aug. 2004.
3. Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen and Ming Ouhyoung, “On Visual Similarity Based 3D Model Retrieval”, Eurographics 2003, Granada, Spain, Sep. 2003 (also in Computer Graphics Forum, Vol. 22, No. 3).
4. Wan-Chun Ma, Fu-Che Wu, Ming Ouhyoung, “Skeleton Extraction of 3D Objects with Radial Basis Functions”, Shape Modeling International 2003, Seoul, Korea, May 2003.
5. Pin-Chou Liu, Fu-Che Wu, Wan-Chun Ma, Rung-Huei Liang, Ming Ouhyoung,
“Automatic Animation Skeleton Construction Using Repulsive Force Field” Pacific Graphics 2003 (poster section, accepted), Canmore, Alberta, Canada, Oct. 2003.
6. Fu-Che Wu, Wan-Chun Ma, Ping-Chou Liou, Rung-Huei Laing, Ming Ouhyoung,
“Skeleton Extraction of 3D Objects with Visible Repulsive Force”, currently submitted.
7. Ding-Yun Chen, “On Visual Similarity Based 3D Model Retrieval”, Ph.D. thesis, Dept.
of Computer Science and Information Engineering.
8. Xiao-Pei Tian, “A 3D Model Retrieval System Based on MPEG-7 3DSSD”, Master thesis, Dept. of Computer Science and Information Engineering.
9. Shuen-Huei Guan, “A Feature Preserving Multiresolution Volumetric Sculpting System”, Master thesis, Dept. of Computer Science and Information Engineering.
10. Shung-Yao Cho, “Constructing Scalable 3D Animated Model by Deformation Sensitive Simplification”, Master thesis, Dept. of Computer Science and Information Engineering.
3-14
On Visual Similarity Based 3D Model Retrieval
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen and Ming Ouhyoung Department of Computer Science and Information Engineering,
National Taiwan University, Taipei, Taiwan
{dynamic, babylon, edwards}@cmlab.csie.ntu.edu.tw, [email protected]
Abstract
A large number of 3D models are created and available on the Web, since more and more 3D modelling and digitizing tools are developed for ever increasing applications. The techniques for content-based 3D model re-trieval then become necessary. In this paper, a visual similarity-based 3D model rere-trieval system is proposed.
This approach measures the similarity among 3D models by visual similarity, and the main idea is that if two 3D models are similar, they also look similar from all viewing angles. Therefore, one hundred orthogonal projections of an object, excluding symmetry, are encoded both by Zernike moments and Fourier descriptors as features for later retrieval. The visual similarity-based approach is robust against similarity transformation, noise, model de-generacy etc., and provides 42%, 94% and 25% better performance (precision-recall evaluation diagram) than three other competing approaches: (1)the spherical harmonics approach developed by Funkhouser et al., (2)the MPEG-7 Shape 3D descriptors, and (3)the MPEG-7 Multiple View Descriptor. The proposed system is on the Web for practical trial use (http://3d.csie.ntu.edu.tw), and the database contains more than 10,000 publicly available 3D models collected from WWW pages. Furthermore, a user friendly interface is provided to retrieve 3D models by drawing 2D shapes. The retrieval is fast enough on a server with Pentium IV 2.4GHz CPU, and it takes about 2 seconds and 0.1 seconds for querying directly by a 3D model and by hand drawn 2D shapes, respectively.
Categories and Subject Descriptors(according to ACM CCS): H.3.1 [Information Storage and Retrieval]: Indexing Methods
1. Introduction
Recently, the development of 3D modeling and digitizing technologies has made the model generating process much easier. Also, through the Internet, users can download a large number of free 3D models from all over the world. This leads to the necessities of a 3D model retrieval system. Although text-based search engines are ubiquitous today, multimedia data, such as 3D models, usually lacks meaningful descrip-tion for automatic matching. The MPEG group aims to cre-ate an MPEG-7 international standard, also known as "Mul-timedia Content Description Interface", for the description of multimedia data11. However, little description is about 3D models. The need of developing efficient techniques for content-based 3D model retrieval is increasing.
To search 3D models that are visually similar to a queried model is the most intuitive way. However, most meth-ods concentrate on the similarity of geometric distributions rather than directly searching for visually similar models.
The geometric-based approach is feasible since much ap-pearance for an object is controlled by its geometry. In this paper, however, we present a novel approach that matches 3D models using their visual similarities, which are mea-sured with image differences in light fields. We take this ap-proach to better fit the goal of comparing models that appear to be similar to a human observer. The concept of the vi-sual similarity-based approach is similar to that of the image-driven simplification, proposed by Lindstrom and Turk14.
The geometry-based approach is broadly classified into two categories: shape-based and topology-based matching.
The shape-based approach uses the distribution of vertices or polygons to judge the similarity between 3D models
1,2,4,5,6,7. The challenge of the shape-based approach is how to define shape descriptors, which need to be sensitive, unique, stable, efficient, and robust against similarity trans-formations of various kinds of 3D models. The topology-based approach utilizes topological structures of 3D models
c
The Eurographics Association and Blackwell Publishers 2003. Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.
body. The topologies are quite different whether the finger does or does not connect to a human body, but the shapes are similar.
Most previous works of 3D model retrieval focused on defining suitable features for the matching process1∼13, and were based on either statistical properties, such as global shape histograms, or the skeletal structures of 3D models.
Osada et al. 2 proposed and analyzed a method for com-puting shape signatures of arbitrary 3D polygonal models.
The key idea is to represent the signature of a 3D model as a shape distribution, which is a histogram created from the distance between two random points on a surface for mea-suring global geometric properties. The approach is simple, fast and robust, and could be applied as a pre-classifier in a complete 3D model retrieval system.
Funkhouser et al.1proposed a practical web-based search engine that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For 3D shape queries, a new matching algorithm that uses spherical har-monics to compute similarities is developed. It does not re-quire repair of model degeneracy or alignment of orienta-tions. In their system, a multimodal query is applied to in-crease the retrieval performance by combining features such as text and 3D shapes. It is also fast enough to retrieve from a repository of 20,000 models in less than one second.
Hilaga et al.3proposed a technique in which the similarity between polyhedral models is accurately and automatically calculated by comparing the skeletal and topological struc-ture. The skeletal and topological structure decomposes a 3D model to a one-dimensional graph structure. The graph is in-variant to similarity transformations, robust against simplifi-cation and deformation caused by changing posture of an articulated object, etc. In their experimental results, the av-erage search time from 230 3D models is about 12 seconds with a Pentium II 400MHz processor. Another 3D model retrieval system10, having 445 models in the database, is extended from the work of Hilaga et al., and takes about 12 seconds on a server with Pentium IV 2.4 GHz processor.
In this paper, a novel visual similarity-based approach
2. Feature Extraction for Representing 3D Models