3D Car Shape Reconstruction from a Single Sketch Image

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketch image. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deep neural network that takes a 2D sketch and generates a set of multiview depth & mask images, which are more effective representation comparing to 3D mesh, and can be combined to form the 3D car shape. To ensure the volume and diversity of the training data, we propose a feature-preserving car mesh augmentation pipeline for data augmentation. Since deep learning has limited capacity to reconstruct fine-detail features, we propose a lazy learning approach that constructs a small subspace based on a few relevant car samples in the database. Due to the small size of such a subspace, fine details can be represented effectively with a small number of parameters. With a low-cost optimization process, a high-quality car with detailed features is created. Experimental results show that the system performs consistently to create highly realistic cars of substantially different shape and topology, with a very low computational cost

Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketchimage. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deepneural network that takes a 2D sketch and generates a set of multi-view depth and mask images, which forma more effective representation comparing to 3D meshes, and can be effectively fused to generate a 3D carshape. Since global models like deep learning have limited capacity to reconstruct fine-detail features, wepropose a local lazy learning approach that constructs a small subspace based on a few relevant car samples inthe database. Due to the small size of such a subspace, fine details can be represented effectively with a smallnumber of parameters. With a low-cost optimization process, a high-quality car shape with detailed featuresis created. Experimental results show that the system performs consistently to create highly realistic cars ofsubstantially different shape and topology