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Authors: Naoki Nozawa 1 ; Hubert P. H. Shum 2 ; Edmond S. L. Ho 2 and Shigeo Morishima 3

Affiliations: 1 Department of Pure and Applied Physics, Waseda University, Tokyo, Japan ; 2 Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K. ; 3 Waseda Research Institute for Science and Engineering, Waseda University, Tokyo, Japan

Keyword(s): Deep Learning, Lazy Learning, 3D Reconstruction, Sketch-based Interface, Car.

Abstract: 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. Experimen tal results show that the system performs consistently to create highly realistic cars of substantially different shape and topology. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Nozawa, N.; Shum, H.; Ho, E. and Morishima, S. (2020). Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - GRAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 179-190. DOI: 10.5220/0009157001790190

@conference{grapp20,
author={Naoki Nozawa. and Hubert P. H. Shum. and Edmond S. L. Ho. and Shigeo Morishima.},
title={Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - GRAPP},
year={2020},
pages={179-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009157001790190},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - GRAPP
TI - Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning
SN - 978-989-758-402-2
IS - 2184-4321
AU - Nozawa, N.
AU - Shum, H.
AU - Ho, E.
AU - Morishima, S.
PY - 2020
SP - 179
EP - 190
DO - 10.5220/0009157001790190
PB - SciTePress