Jun Yang
ABSTRACT
The co-segmentation of a set of 3D shapes plays an important role in understanding and analyzing the 3D shapes. An unsupervised co-segmentation algorithm of 3D shapes based on components is proposed in view of the low efficiency and poor accuracy in the existing co-segmentation algorithms. Firstly, all the input models are pre-partitioned into meaningful components by calculating the conformal factor. Secondly, the 3D model is converted into a statistical model, and every column of the statistical model is used to represent different component unit, and then the corresponding relationship between different components is constructed and marked via Gaussian kernel function. Finally, dynamic k-means clustering algorithm is employed to realize meaningful co-segmentation. Experimental result demonstrates that the proposed algorithm can achieve co-segmentation of 3D model cluster with the same function but different postures and the obtained segmentation is convenient for further geometric analysis.
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Index Terms
- Unsupervised Co-segmentation of 3D Shapes Based on Components
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