Abstract: A novel mesh segmentation algorithm based on 3D Delaunay triangulation is presented to partition meshes meaningfully.A volume-based distance(VD) for each face is first computed using 3D Delaunay triangulation.After a smoothing process,clustering of the mesh faces is performed to extract k clusters based on their VD values: a Gaussian mixture model(GMM) fitting k Gaussians to the histogram of VD values of the faces,this is achieved using the expectation-maximization(EM) algorithm.Finally,by considering the quality of clustering,the smoothness of the partition boundary and the minima rule proposed in human cognitive vision theory,our method employs a graph-cut algorithm to get the meaningful partitioning.Experiment indicates that the method is efficient and can partition a mesh into meaningful parts.