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

Naoiki Nozawa, Hubert Shum*, Qi Feng, Edmond S. L. Ho, Shigeo Morishima

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
56 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1317–1330
Number of pages14
JournalThe Visual Computer
Issue number4
Early online date16 Apr 2021
Publication statusPublished - 1 Apr 2022


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