3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning

Naoki Nozawa, Hubert P. H. Shum, Qi Feng, Edmond S. L. Ho and Shigeo Morishima
Visual Computer (VC), 2022

Impact Factor: 2.835# Citation: 12##

3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning
# Impact factors from the Journal Citation Reports 2021
## Citation counts from Google Scholar as of 2022

Abstract

3D car models are heavily used in computer games, visual effects, and even automotive designs. As a result, producing such models with minimal labour costs is increasingly more important. To tackle the challenge, we propose a novel system to reconstruct a 3D car using a single sketch image. The system learns from a synthetic database of 3D car models and their corresponding 2D contour sketches and segmentation masks, allowing effective training with minimal data collection cost. The core of the system is a machine learning pipeline that combines the use of a Generative Adversarial Network (GAN) and lazy learning. GAN, being a deep learning method, is capable of modelling complicated data distributions, enabling the effective modelling of a large variety of cars. Its major weakness is that as a global method, modelling the fine details in the local region is challenging. Lazy learning works well to preserve local features by generating a local subspace with relevant data samples. We demonstrate that the combined use of GAN and lazy learning produces is able to produce high-quality results, in which different types of cars with complicated local features can be generated effectively with a single sketch. Our method outperforms existing ones using other machine learning structures such as the variational autoencoder.

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BibTeX

@article{nozawa21car,
 author={Nozawa, Naoki and Shum, Hubert P. H. and Feng, Qi and Ho, Edmond S. L. and Morishima, Shigeo},
 journal={Visual Computer},
 title={3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning},
 year={2022},
 volume={38},
 number={4},
 pages={1317--1330},
 numpages={14},
 doi={10.1007/s00371-020-02024-y},
 issn={1432-2315},
 publisher={Springer},
}

RIS

TY  - JOUR
AU  - Nozawa, Naoki
AU  - Shum, Hubert P. H.
AU  - Feng, Qi
AU  - Ho, Edmond S. L.
AU  - Morishima, Shigeo
T2  - Visual Computer
TI  - 3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning
PY  - 2022
VL  - 38
IS  - 4
SP  - 1317
EP  - 1330
DO  - 10.1007/s00371-020-02024-y
SN  - 1432-2315
PB  - Springer
ER  - 

Plain Text

Naoki Nozawa, Hubert P. H. Shum, Qi Feng, Edmond S. L. Ho and Shigeo Morishima, "3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning," Visual Computer, vol. 38, no. 4, pp. 1317-1330, Springer, 2022.

Similar Research

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
Naoki Nozawa, Hubert P. H. Shum, Edmond S. L. Ho and Shigeo Morishima, "3D Car Shape Reconstruction from a Single Sketch Image", Proceedings of the 2019 International Conference on Motion, Interaction and Games (MIG) Posters, 2019

 

 

Last updated on 19 January 2023
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