Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common topological structure, such as human faces and bodies. Morphable Models and their variants, despite their linear formulation, have been widely used for shape representation, while most of the recently proposed nonlinear approaches resort to intermediate representations, such as 3D voxel grids or 2D views. In this work, we introduce a novel graph convolutional operator, acting directly on the 3D mesh, that explicitly models the inductive bias of the fixed underlying graph. This is achieved by enforcing consistent local orderings of the vertices of the graph, through the spiral operator, thus breaking the permutation invariance property that is adopted by all the prior work on Graph Neural Networks. Our operator comes by construction with desirable properties (anisotropic, topology-aware, lightweight, easy-to-optimise), and by using it as a building block for traditional deep generative architectures, we demonstrate state-of-the-art results on a variety of 3D shape datasets compared to the linear Morphable Model and other graph convolutional operators.Comment: to appear at ICCV 201

Converting a CAD Model into a Manufacturing Model for the Components Made of a Multiphase Perfect Material

To manufacture the component made of a multiphase perfect material (including homogeneous and multi heterogeneous materials), it CAD model should be processed and converted into layered manufacturing model for further transformation of numerical control (NC) coding. This paper develops its detailed approaches and corresponding software. The process planning is made first and includes: (1) determining the build orientation of the component; and (2) slicing the component into layers adaptively according to different material regions since different materials have different optimal layer thickness for manufacturing. After the process planning, the layered manufacturing models with necessary information, including fabrication sequence and material information of each layer, are fully generated.Mechanical Engineerin