Cloud Sphere:一种基于渐进式变形自编码的三维模型表征方法  

Cloud Sphere:a 3D shape representation method via progressive deformation

作　　者：王宗继 刘云飞 陆峰 WANG Zongji;LIU Yunfei;LU Feng(Key Laboratory of Target Cognition and Application Technology,Key Laboratory of Network Information System Technology,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100190,China;State Key Laboratory of Virtual Reality Technology and Systems,School of Computer Science and Engineering,Beihang Univer sity,Beijing 100191,China)

机构地区：[1]中国科学院空天信息创新研究院,目标认知与应用技术重点实验室,网络信息体系技术重点实验室,北京100190 [2]北京航空航天大学计算机学院,虚拟现实技术与系统国家重点实验室,北京100191

出　　处：《图学学报》2024年第6期1375-1388,共14页Journal of Graphics

基　　金：“十四五”共用信息系统装备预研项目(31511060301)。

摘　　要：针对大数据时代三维模型形状多样性激增的挑战,致力于从形状形成过程中发现独特信息,提出了一种基于球表面逐步变形对三维模型的形状进行统一表征的方法。输入任意三维模型,通过逐步变形自编码网络将一个模板球面点云逐步变形拟合该输入形状。通过深度神经网络建模三维模型变形过程,从多阶段变形中挖掘独特的形状特征,避免了任务驱动学习方法对人工标注的依赖。通过显式编码形状生成过程中的变形残差,不仅捕捉了最终形状,还记录了形状的渐进变化过程。在深度神经网络的训练方面,采用了多阶段信息监督的方式,提高了变形重建的精度。与当前技术水平代表方法的对比实验表明,多阶段监督训练方式能够增强变形重建结果的细节精度。丰富的消融实验验证了多阶段监督方式的有效性。变形表征方法适用于模型分类、形状迁移、共编辑等计算机图形学应用,具有泛用性,可为三维模型几何属性自动解析与高效编辑提供底层的数据表征方法支持。As 3D data proliferates,3D models are exhibiting increasingly diverse and complex shapes.Dedicated to discovering distinctive information from the shape formation process,a method has been developed to uniformly represent the shapes of 3D models through progressive deformation.For any input 3D model,a spherical point cloud template was gradually deformed to fit the input shape through a coarse-to-fine progressive deformation-based auto-encoder.The 3D shape deformation process was modeled using deep neural networks,extracting unique shape features from the multi-stage deformation process and avoiding the reliance on manual annotations common in general task-driven learning methods.The deformation residuals during the shape generation process were explicitly encoded.It not only captured the final shape but also recorded the progressive deformation process from the initial state to the final shape.In terms of deep neural network training,a multi-stage information supervision approach was developed for feature learning,improving the accuracy of deformation reconstruction.Experimental results showed that the proposed method has the ability to reconstruct 3D shapes with high fidelity,and consistent topology was preserved in the multi-stage deformation process.This deformation representation is applicable to various computer graphics applications such as model classification,shape transfer,and co-editing,demonstrating versatility and providing underlying data representation method support for automatic parsing and efficient editing of 3D model geometric properties.

关 键 词：三维表征 三维模型变形 球面点云模板 自编码器 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]