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ACM Transactions on Graphics (TOG)
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SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
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SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
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IEEE Computer Graphics and Applications
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ACM Transactions on Graphics (TOG)
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MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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Computer-Aided Design
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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Pattern Recognition Letters
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Multimedia Tools and Applications
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MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
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WSEAS Transactions on Circuits and Systems
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Computer-Aided Design
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MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
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ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
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International Journal of Computer Vision
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Frontiers of Computer Science in China
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The Visual Computer: International Journal of Computer Graphics
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In this paper, we propose an algorithm for shape-similarity comparison and retrieval of 3D shapes defined as polygon soup. One of the issues in comparing 3D shapes is the diversity of shape representations used to represent these "3D" shapes. While a solid model is well-defined and is easier to handle, others such as polygon soup poses many problems. In fact, a polygon soup 3D model most often does not define a 3D shape, but merely an illusion of "3D shape-ness" by its collection of independent polygons, lines, and manifold meshes. The most significant feature of our 3D shape similarity comparison method is that it accepts polygon soup and other ill-defined 3D models. Our approach is to use the rendered appearance only of the model as the basis for shape similarity comparison. Our method removes scale and positional degrees-of-freedom by using normalization, and the three rotational degrees of freedom by using a combination of discrete sampling of solid angles and a rotation-invariant 2D image similarity comparison algorithm. Evaluation experiments showed that, despite its simplicity, our approach worked quite well despite its simplicity.