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Virtual View Generation Based on 3D-Dense-Attentive GAN Networks
A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358985/ https://www.ncbi.nlm.nih.gov/pubmed/30654544 http://dx.doi.org/10.3390/s19020344 |
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author | Fu, Junwei Liang, Jun |
author_facet | Fu, Junwei Liang, Jun |
author_sort | Fu, Junwei |
collection | PubMed |
description | A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutional neural networks (3D-CNN) and attentive mechanisms is introduced to extract the time-series features from image sequences. To avoid gradient vanish during training, the dense block structure is utilized to improve the discriminator network. Meanwhile, three kinds of image features, including image edge, depth map and optical flow are extracted to constrain the supervised training of model. The final results on KITTI and Cityscapes datasets demonstrate that our algorithm outperforms conventional methods, and the missing vision signal can be replaced by a generated virtual view. |
format | Online Article Text |
id | pubmed-6358985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63589852019-02-06 Virtual View Generation Based on 3D-Dense-Attentive GAN Networks Fu, Junwei Liang, Jun Sensors (Basel) Article A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutional neural networks (3D-CNN) and attentive mechanisms is introduced to extract the time-series features from image sequences. To avoid gradient vanish during training, the dense block structure is utilized to improve the discriminator network. Meanwhile, three kinds of image features, including image edge, depth map and optical flow are extracted to constrain the supervised training of model. The final results on KITTI and Cityscapes datasets demonstrate that our algorithm outperforms conventional methods, and the missing vision signal can be replaced by a generated virtual view. MDPI 2019-01-16 /pmc/articles/PMC6358985/ /pubmed/30654544 http://dx.doi.org/10.3390/s19020344 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fu, Junwei Liang, Jun Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title | Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title_full | Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title_fullStr | Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title_full_unstemmed | Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title_short | Virtual View Generation Based on 3D-Dense-Attentive GAN Networks |
title_sort | virtual view generation based on 3d-dense-attentive gan networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358985/ https://www.ncbi.nlm.nih.gov/pubmed/30654544 http://dx.doi.org/10.3390/s19020344 |
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