Fast Mesh Segmentation Based on Multiple Kernel Learning

Mesh segmentation is one of the most important problems in 3D geometry analysis. It not only plays an essential role in traditional areas such as modeling and rendering, but also is a foundation of high-level geometry understanding. This paper presents a fast 3D mesh segmentation algorithm based on Multiple Kernel Learning. The Multiple Kernel Learning method deals with the diversity of features in mesh segmentation by using a combination of kernels instead of a specified kernel. Given a set of well-segmented meshes of a category, the algorithm first over-segments the meshes, then trains a classifier using Multiple Kernel Learning. After that, the classifier is used to segment other un-segmented meshes. In the algorithm procedure, over segmentation reduced the number of training samples, and Multiple Kernel Learning enhanced the ability to reveal the relationships among the data. Results show our algorithm not only outperforms the state-of-the-art mesh segmentation method, but also maintain a high computational speed, which makes it suitable for real-time segmentation learning.