3D mesh segmentation has become an essential step in many applications in 3D shape analysis.In this paper, a new segmentation method is proposed based on a learning approach using the artificial neural networks classifier and the spectral clustering for segmentation.Firstly, a training step is done using the artificial neural network trained on existing segmentation, taken from the ground truth segmentation (done by humane operators) available in the where to buy opi nail polish toronto benchmark proposed by Chen et al.to extract the candidate mercury metallic lexus boundaries of a given 3D-model based on a set of geometric criteria.Then, we use this resulted knowledge to construct a new connectivity of the mesh and use the spectral clustering method to segment the 3D mesh into significant parts.
Our approach was evaluated using different evaluation metrics.The experiments confirm that the proposed method yields significantly good results and outperforms some of the competitive segmentation methods in the literature.