Abstract: Data-driven supervised co-segmentation can achieve more accurate segmentation and labeling requirements based on prior knowledge. However, most of supervised methods are extremely time-consuming and difficult to scale up to large data set. The fast 3D shape segmentation and labeling learning method via extreme learning machine is provided, which trains facets and edges simultaneously. Based on that, graph-cut is adopted to smooth and optimize the segmentation boundaries.  The experimental results show that this method can learn quickly and achieve high segmentation accuracy and visual effect in the process of 3D shape segmentation and labeling.

Key words: hyperchaotic, encryption, three-dimensional, mesh model

摘要： 数据驱动的有监督联合分割可以通过先验知识的学习，达到更精确的分割与标注要求。然而，目前的有监督分割方法大多需要耗费大量的训练时间，不利于大规模数据集的扩展。为了提高学习效率，提出一种基于极限学习机同时对面片和网格边进行训练的快速的三维形状分割和标注方法。进而通过图割优化进行分割边缘的平滑和优化，得到最终的标注结果。实验结果表明，在三维形状的分割和标注过程中，该方法学习快速，且可以达到较高的分割精度和视觉效果。

关键词: 超混沌, 加密, 三维, 网格模型