3D Car Shape Reconstruction from a Single Sketch Image

Nozawa, Naoiki, Shum, Hubert, Ho, Edmond and Morishima, Shigeo (2019) 3D Car Shape Reconstruction from a Single Sketch Image. In: MIG 2019 : 12th annual ACM/SIGGRAPH conference on Motion, Interaction and Games, 28-30 Oct 2019, Newcastle upon Tyne, England. (In Press)

[img]
Preview
Text
Nozawa et al - 3D Car Shape Reconstruction from a Single Sketch Image AAM.pdf - Accepted Version

Download (1MB) | Preview

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 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.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: Deep Learning, Lazy Learning, 3D Reconstruction, Sketch-based Interface, Car
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 01 Oct 2019 11:17
Last Modified: 11 Oct 2019 13:32
URI: http://nrl.northumbria.ac.uk/id/eprint/40941

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence