摘要
目的3D形状分析是计算机视觉和图形学的一个重要研究课题。虽然现有方法使用基于图的卷积将基于图像的深度学习推广到3维网格,但缺乏有效的池化操作限制了其网络的学习能力。针对具有相同连通性,但几何形状不同的网格模型数据集,本文利用网格简化的边收缩操作建立网格层次结构,提出了一种新的网格池化操作。方法本文改进了传统的网格简化方法,以避免生成高度不规则的三角形,利用改进的网格简化方法定义了新的网格池化操作。网格简化的边收缩操作建立的网格层次结构之间存在对应关系,有利于网格池化的定义。新定义的池化操作有效地编码了层次结构中较粗糙和较稠密网格之间的对应关系。最后提出了一种带有边收缩池化和图卷积的变分自编码器(variational auto-encoder,VAE)结构,以探索3D形状的隐空间并用于3D形状的生成。结果由于引入了新定义的池化操作和图卷积操作,提出的网络结构比原始MeshVAE需要的参数更少,因此可以处理更稠密的网格模型。结论实验表明提出的方法具有更好的泛化能力,并且在各种应用中更可靠,包括形状生成、形状插值和形状嵌入。
Objective3 D shape datasets have been tremendous facilitated nowadays.Data-driven 3 D shape analysis has been an active research topic in computer vision and graphics.Apart from regular works,current data-driven works attempted to generalize deep neural networks from images to 3 D shapes,including triangular meshes,point clouds and voxel data.Deep neural networks for triangular meshes have been concentrated.3 D meshes have complicated and irregular inter-connection.Most current works tend to keep mesh connectivity unchanged each layer,thus,losing the capability of increased receptive fields when pooling operations are applied.The variational auto-encoder(VAE)has been widely used in various kinds of generation tasks,including generation,interpolation and exploration on triangular meshes.Based on a fully-connected network,the initial MeshVAE requires mega parameters and its generalization capability is often weak.Although the fully connected layers allow changes of mesh connectivity across layers,due to irregular changes,such approaches cannot be directly generalized to convolutional layers.Some works adopt convolutional layers in the VAE structure.However,such convolution operations cannot change the connectivity of the mesh.Sampling operation is also evolved in convolutional neural networks(CNNs)on meshes,but the mesh sampling strategy does not aggregate the whole local neighborhood information when reducing the quantities of vertices.Hence,it is necessary to design a pooling operation for meshes similar to the pooling for images to reduce the amount of network parameters in order to deal with denser models and enhance the generalization ability of the network.Moreover,the defined pooling can support further convolutions and conduct recovery via a corresponding de-pooling operation.MethodA novel mesh pooling operation is illustrated based on edge contraction.The VAE architecture in context of the newly defined pooling operation is built up as well.Mesh simplification is applied to organize a mesh hierarchy with d
作者
袁宇杰
来煜坤
杨洁
段琦
傅红波
高林
Yuan Yujie;Lai Yukun;Yang Jie;Duan Qi;Fu Hongbo;Gao Lin(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Computer Science and Informatics,Cardiff University,Cardiff CF244AG,UK;SenseTime Research,Shanghai 200233,China;City University of Hong Kong,Hong Kong 999077,China)
出处
《中国图象图形学报》
CSCD
北大核心
2022年第2期511-524,共14页
Journal of Image and Graphics
基金
国家自然科学基金项目(62061136007,61872440)
北京市自然科学基金项目(L182016)
英国皇家学会牛顿高级学者基金项目(NAFR2192151)
中国科学院青年创新促进会基金项目(2019108)
之江实验室开放课题基金项目(2021KE0AB06)。
