3D car shape reconstruction from a single sketch image

Naoiki Nozawa, Hubert P.H. Shum, Edmond S.L. Ho, Shigeo Morishima

研究成果: Conference contribution

1 被引用数 (Scopus)


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.

ホスト出版物のタイトルProceedings - MIG 2019
ホスト出版物のサブタイトルACM Conference on Motion, Interaction, and Games
編集者Stephen N. Spencer
出版社Association for Computing Machinery, Inc
出版ステータスPublished - 2019 10月 28
イベント2019 ACM Conference on Motion, Interaction, and Games, MIG 2019 - Newcastle upon Tyne, United Kingdom
継続期間: 2019 10月 282019 10月 30


名前Proceedings - MIG 2019: ACM Conference on Motion, Interaction, and Games


Conference2019 ACM Conference on Motion, Interaction, and Games, MIG 2019
国/地域United Kingdom
CityNewcastle upon Tyne

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 人間とコンピュータの相互作用
  • 教育


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