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)
ACM Transactions on Graphics (TOG)
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Automatic Class Selection and Prototyping for 3-D Object Classification
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Efficient Computation of Isometry-Invariant Distances Between Surfaces
SIAM Journal on Scientific Computing
Topology driven 3D mesh hierarchical segmentation
SMI '07 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
International Journal of Computer Vision
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A comparison framework for 3d object classification methods
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
A Bayesian 3-D Search Engine Using Adaptive Views Clustering
IEEE Transactions on Multimedia
Content-Based Retrieval of 3-D Objects Using Spin Image Signatures
IEEE Transactions on Multimedia
Combining Topological and Geometrical Features for Global and Partial 3-D Shape Retrieval
IEEE Transactions on Multimedia
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image classification for content-based indexing
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Grouping 3D objects into (semantically) meaningful categories is a challenging and important problem in 3D mining and shape processing. Here, we present a novel approach to categorize 3D objects. The method described in this article, is a belief-function-based approach and consists of two stages: the training stage, where 3D objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca-Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D models.