Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
Abstract
As 3D movie viewing becomes mainstream and the Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks to automatically convert 2D videos and images to a stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from existing 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations.
Cite
Text
Xie et al. "Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46493-0_51Markdown
[Xie et al. "Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/xie2016eccv-deep/) doi:10.1007/978-3-319-46493-0_51BibTeX
@inproceedings{xie2016eccv-deep,
title = {{Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks}},
author = {Xie, Junyuan and Girshick, Ross B. and Farhadi, Ali},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {842-857},
doi = {10.1007/978-3-319-46493-0_51},
url = {https://mlanthology.org/eccv/2016/xie2016eccv-deep/}
}