Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
ACM Transactions on Graphics (TOG)
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Skeleton Based Shape Matching and Retrieval
SMI '03 Proceedings of the Shape Modeling International 2003
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
SMI '04 Proceedings of the Shape Modeling International 2004
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
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With the rapid increase of available 3D models, content-based 3D retrieval is attracting more and more research interests. One of the key problems in content-based 3D retrieval is to extract discriminative features for measuring the similarity and dissimilarity between different shapes. In this paper, we define 3D Krawtchouk moments for 3D shape analysis and retrieval. Differing with 3D Zernike moments deduced from continuous orthogonal polynomials, the basis of 3D Krawtchouk moments is discrete orthogonal polynomial. It has some interesting property for describing shape information and retrieving 3D models, such as multi-resolution, high-computation, simplification and so on. To verify the advantage of 3D Krawtchouk moments, experiments are carried out to compare the retrieving performance based on Krawtchouk moments and Zernike moments. The results have proven that Krawtchouk moments can achieve better retrieving accuracy and efficiency.