Scene understanding for autonomous vehicles with deep learning
This project aimed to carry out algorithm which is able to generate semantic images and then fuse the semantic images with point cloud to obtain point cloud with semantic information for the scene understanding tasks. This project can be seen as a base for scene understanding task, which covers a...
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Nanyang Technological University
2022
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sg-ntu-dr.10356-1594062023-07-04T17:45:02Z Scene understanding for autonomous vehicles with deep learning Wang, Dongying Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering This project aimed to carry out algorithm which is able to generate semantic images and then fuse the semantic images with point cloud to obtain point cloud with semantic information for the scene understanding tasks. This project can be seen as a base for scene understanding task, which covers a wide range of data designing and processing work. First of all, labeling strategies designed for semantic segmentation aimed to help with scene understanding tasks have been carried out. In addition, for semantic information generation, a semantic segmentation network integrating attention mechanism and all MLP layers upsampling technique was applied. Finally, to get abundant information, the semantic images are fused with the point cloud using an algorithm based on robotics geometric projection theories. Master of Science (Computer Control and Automation) 2022-06-16T04:39:50Z 2022-06-16T04:39:50Z 2022 Thesis-Master by Coursework Wang, D. (2022). Scene understanding for autonomous vehicles with deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159406 https://hdl.handle.net/10356/159406 en application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Wang, Dongying Scene understanding for autonomous vehicles with deep learning |
description |
This project aimed to carry out algorithm which is able to generate semantic
images and then fuse the semantic images with point cloud to obtain point
cloud with semantic information for the scene understanding tasks. This project
can be seen as a base for scene understanding task, which covers a wide range
of data designing and processing work. First of all, labeling strategies designed
for semantic segmentation aimed to help with scene understanding tasks have
been carried out. In addition, for semantic information generation, a semantic
segmentation network integrating attention mechanism and all MLP layers
upsampling technique was applied. Finally, to get abundant information, the
semantic images are fused with the point cloud using an algorithm based on
robotics geometric projection theories. |
author2 |
Wang Dan Wei |
author_facet |
Wang Dan Wei Wang, Dongying |
format |
Thesis-Master by Coursework |
author |
Wang, Dongying |
author_sort |
Wang, Dongying |
title |
Scene understanding for autonomous vehicles with deep learning |
title_short |
Scene understanding for autonomous vehicles with deep learning |
title_full |
Scene understanding for autonomous vehicles with deep learning |
title_fullStr |
Scene understanding for autonomous vehicles with deep learning |
title_full_unstemmed |
Scene understanding for autonomous vehicles with deep learning |
title_sort |
scene understanding for autonomous vehicles with deep learning |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/159406 |
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1772827207118356480 |