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
Key words
3D representation/3D deformation/spherical point clouds template/auto-encoder/deep learning
维普
万方数据