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 multi-view depth and mask images, which form a more effective representation comparing to 3D meshes, and can be effectively fused to generate a 3D car shape. Since global models like deep learning have limited capacity to reconstruct fine-detail features, we propose a local 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 shape with detailed features is created. Experimental results show that the system performs consistently to create highly realistic cars of substantially different shape and topology.
Naoki Nozawa, Hubert P. H. Shum, Edmond S. L. Ho and Shigeo Morishima,
"Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning",
Proceedings of the 2020 International Conference on Computer Graphics Theory and Applications (GRAPP), 2020
Citation: 1## Best Student Paper Award
TY - CONF
Naoki Nozawa, Hubert P. H. Shum, Edmond S. L. Ho and Shigeo Morishima, "Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning," in GRAPP '20: Proceedings of the 2020 International Conference on Computer Graphics Theory and Applications, pp. 179-190, Valletta, Malta, SciTePress, Feb 2020.
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