Naoiki Nozawa, Hubert P. H. Shum, Edmond S. L. Ho, Shigeo Morishima
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 파이썬 64bit. 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 Download Mr. Company. 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 Notepad2 Hangul. 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 Windriver. 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 Go mp3.