Impact Factor: 2.835# Citation: 12##
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.
TY - JOUR
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.
Last updated on 19 January 2023