Naoki Nozawa, Hubert P. H. Shum, Edmond S. L. Ho, Shigeo Morishima
Single Sketch Image based 3D Car Shape Reconstruction with DeepLearning and Lazy Learning
GRAPP 2020: Best Student Paper Award
Efficient car shape design is a challenging problem in both the automotive industry and the computer anima-tion/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketchimage 전투기 게임. 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 Download ssh secure shell. 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 Download Gumrod. 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 제 3 성전. Experimental results show that the system performs consistently to create highly realistic cars ofsubstantially different shape and topology.