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Domains 3-D Sensing and Modelling Virtual Reality and Simulation Infrared Thermography and Vision Video Sequence Processing Biologic Vision Cognitive Vision Projects E-Inclusion COBVIS-D Auto21 SKALPEL-ICT COGNOIS VERTEX VRES Researchers Christian Gagné Xavier Maldague Denis Poussart Simon Gagné Marc Parizeau Robert Bergevin André Zaccarin Jean-François Lalonde Abdel Hakim Bendada Dates Reverse chronological order |
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3D-NCuts: Adapting Normalized Cuts to 3D Triangulated Surface SegmentationAbstract - Being able to automatically segment 3D models into meaningful parts is an important goal in 3D shape processing. In this paper, we are proposing a fast and easy-to-implement 3D segmentation approach, which is based on spectral clustering. For this purpose, we define an improved formulation of the similarity matrix which allows our algorithm to segment both free-form and CAD (Computer Aided Design) 3D models. In 3D space, different shapes, such as planes and cylinders, have different surface normal distributions. We defined the similarity of vertices based on their normals which can segment a 3D model into its geometric features. Results show the effectiveness and robustness of our method in segmenting a wide range of 3D models. Even in the case of complex models, our method results in meaningful segmentations. We tested our segmentation approach on real data segmentation, in the presence of noise and also in comparison with other methods which provided good results in all cases. ![]() Bibtex:
@inproceedings{Toony1029, Last modification: 2014/03/21 by cgagne |
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