Dissertation
Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices

3D Model Shape Analysis and Retrieval

Author PanXiang
Tutor YeXiuZuo
School Zhejiang University
Course Applied Computer Technology
Keywords Content-based 3D Retrieval Symmetry Radius-Angle Histogram 3D Krawtchouk Moment Segmentation Structure Features Bipartite Graph Multi-stage Retrieving Architecture Feedback
CLC TP391.4
Type PhD thesis
Year 2005
Downloads 880
Quotes 26
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Recent development in modeling and digitizing techniques has led to a rapid increase of 3D models, and more and more 3D free models can be accessed from Internet or from other resources. 3D model has been widely used in e-business, virtual environment and product design etc. An urgent problem is how to help people find their desirable 3D models accurately and efficiently from the model databases or from the web. Text-based retrieval has its limitation due to the difficulty of describing the shape content by text. Content-based 3D retrieval aiming to retrieve 3D model by shape content has become a hot research topic. The research of this thesis is about content-based 3D model retrieval, mainly 3D shape analysis. The main work can be concluded as following:In 3D model normalization, pose estimation is often performed by Principal Component Analysis(PCA). The algorithm, however, will appear to be unstable for some models. Post-processing using view symmetry is presented to improve the robustness of the PCA based pose estimation. The given model is first normalized by PCA algorithm. The main view of the normalized model is obtained by projection technology. If the view has a strong symmetry along the center line, we adjust the pose result by using the symmetry attribute.In histogram-based shape representation, Radius-Angle Histogram(RAH) is proposed to describe shape contents and used for shape retrieval. The RAH shape descriptor first uses a series of concentric spheres to capture the point distribution information of the given model. Then for points in each concentric sphere, a Radian Normal Angle is computed to extract the local geometry features. Finally, the Radius-Angle Histogram is constructed by using the extracted shape signatures. The proposed shape representation remains invariant under rotation. It can be generated from the given 3D model efficiently and easily as well. Performance comparisons for the shape benchmark database have proven that the proposed algorithm can achieve better retrieving performance than other similar histogram-based shape representations. The thesis also discusses the point sampling result’s affect on the final retrieving precision. The voxelization is used to make the sampled point more even over the surface, and better retrieving precision can be achieved by this process.In moment-based shape representation, we develop a new kind of 3D moment called 3D Krawtchouk moment. It is induced from discrete orthogonal polynomials defined by Krawtchouk.It can provide a multi-resolution representation for the given 3D model. The index table technology is also used to speed up the computation process of 3D Krawtchouk moment. In experiment, a comparison among different kinds of 3D moments, such as Geometrical moment, Zernike moments, has shown 3D Krawtchouk moment can achieve better retrieving performance.To extract structure feature for 3D retrieval, we decompose the given 3D model into some meaningful patches by segmentation technology. To achieve robust segmentation result, flatness measure for mesh faces is defined and used for 3D model segmentation. A two-stage merging strategy is presented to avoid over-merging, a problem often occur in traditional segmentation algorithms. The topological connection graph is constructed from the segmentation result, and used for calculating similarity between 3D models. The thesis further discusses the limitation of the topological connection graph based similarity calculation. Then Bipartite Graph is introduced to compute the similarity between models.Any shape feature, however, can work well for certain sets of models, and no single one can work well for all cases. To improve the retrieving precision greatly, we combine multiple shape features for 3D retrieval. The feedback technology is used to make an optimal combination on different shape features. On the other hand, a multi-stage retrieving architecture is developed to improve the retrieving efficiency based on hybrid shape features. Adaptive threshold is defined to assure that the retrieving system

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