Consistence segmentation of triangle mesh using Laplace spectral embedding and Mean Shift
基于Laplace谱嵌入和Mean Shift的 三角网格一致性分割

In order to overcome the disadvantage of being sensitive to model gesture and noise in the present mesh segmentation algorithms,we present a consistent mesh segmentation algorithm based on Laplace spectral embedding and Mean Shift. We convert mesh into a normal form from the space domain to the spectral domain by using the Laplace-Beltrami operator. The noise is suppressed and spectral embedding enhances the structural segmentability. We adopt Mean Shift,a nonparametric kernel clustering technique,to gain the visual meaningful semantic patch or sub-mesh in the spectral domain. The experiment results show that the proposed algorithm can yield meaningful result rapidly and effectively for meshes which has an evident branch structure.Meanwhile,this approach is invariant to pose of model and robust to noise.