Triangulating a simple polygon in linear time
Discrete & Computational Geometry
Triangulating Simple Polygons and Equivalent Problems
ACM Transactions on Graphics (TOG)
Nearest Neighbor Classification in 3D Protein Databases
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Retrieving 3D shapes based on their appearance
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Indexing and Retrieval of 3D Models by Unsupervised Clustering with Hierarchical SOM
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
ACM SIGGRAPH 2002 conference abstracts and applications
3D model representation using adaptive volumetric extended Gaussian image
Proceedings of the 6th ACM international conference on Image and video retrieval
A Filter-Refinement Scheme for 3D Model Retrieval Based on Sorted Extended Gaussian Image Histogram
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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In this paper, we introduce a novel shape signature, called Volumetric Extended Gaussian Image (VEGI). It captures the volumetric distribution of a 3D mesh model along the latitude-longitude direction without conventional pose normalization and is translation and scaling invariant. Rotation invariance is accomplished by further calculating the spherical harmonic transform of this directional distribution. Due to the completeness and orthonormality properties of spherical harmonics, the VEGI also provides multi-resolution description of a model so that a multi-level indexing scheme based on Hierarchical Self Organizing Map (HSOM) can be established to improve retrieval efficiency. Experimental results show that our retrieval architecture has high discriminative power and outperforms many existing methods.