基于权重能量自适应分布的三维形状分割算法  

3D Shape Segmentation Algorithm Using Weighted Energy Adaptive Distribution

作　　者：舒振宇[1,2] 杨思鹏 辛士庆 庞超逸 杨雨璠 胡超 Shu Zhenyu;Yang Sipeng;Xin Shiqing;Pang Chaoyi;Yang Yufan;Hu Chao(School of Computer and Data Engineering,Ningbo Institute of Technology,Zhejiang University,Ningbo 315100;Ningbo Institute,Zhejiang University,Ningbo 315100;School of Mechanical Engineering,Zhejiang University,Hangzhou 310027;School of Computer Science and Technology,Shandong University,Qingdao 266237;School of Information Science and Engineering,Ningbo Institute of Technology,Zhejiang University,Ningbo 315100)

机构地区：[1]浙江大学宁波理工学院计算机与数据工程学院,宁波315100 [2]浙江大学宁波研究院,宁波315100 [3]浙江大学机械工程学院,杭州310027 [4]山东大学计算机科学与技术学院,青岛266237 [5]浙江大学宁波理工学院信息科学与工程学院,宁波315100

出　　处：《计算机辅助设计与图形学学报》2020年第3期343-351,共9页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61872321,61572022);浙江省自然科学基金(LQ17F030002,LQ19C060001);宁波市自然科学基金(2019A610086);宁波市领军和拔尖人才培养工程择优资助科研项目(NBLJ201801010);宁波市科技计划项目创新团队专项(2014B82015,2016C11024);浙江大学CAD&CG国家重点实验室开放课题(A1702).

摘　　要：针对三维形状分割问题,提出一种引入权重能量自适应分布参与深度神经网络训练的全监督分割算法.首先对三维形状表面进行过分割得到若干小块,提取每一个小块的特征描述符向量作为神经网络的输入,计算权重能量自适应分布,将经过加权后的分割标签作为神经网络的输出,训练深度神经网络.对于新的未分割的三维模型,提取模型表面三角面片的特征向量后输入到神经网络中进行预测分割后,对预测分割的边缘进行修整得到分割结果,实现三维模型的自动分割.在普林斯顿三维模型分割数据集上的实验结果表明,算法通过在训练过程中引入权重能量自适应分布,可以大幅降低神经网络训练时的均方误差,提高神经网络预测结果的准确率;与传统算法相比,该算法具有高准确率、强鲁棒性、强学习扩展能力等优点.In this paper, a fully supervised segmentation algorithm is proposed by using weighted energy adaptive distribution(WEAD). Firstly, the 3 D shapes are divided into several small patches using an over-segmentation method. Secondly, feature vectors of the patches are extracted as the training input and WEADs are used as the training output to re-weight the labels of segmentation. Finally, a corresponding deep neural network is trained. For an unlabeled 3 D shape, our algorithm automatically segments it by using the trained deep neural network followed by the graph cuts method. Extensive experimental results show that the mean square error(MSE) in the training process can be greatly reduced by using WEAD in our algorithm. And our method performs better than other fully supervised and unsupervised algorithms on the Princeton Segmentation Benchmark(PSB) dataset.

关 键 词：三维模型 三维模型分割 权重能量自适应分布 深度学习 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术]