Image-based depth estimation in autonomous driving environment
Depth estimation is one of the critical problems for autonomous vehicles (AVs) in 3D building modeling, SLAM, and localization. One solution is to leverage Radar and LiDAR to capture sparse point clouds, while the hardware is expensive for wide applications. Alternatively, deep learning (DL) network...
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2022
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sg-ntu-dr.10356-1581612023-07-07T19:34:56Z Image-based depth estimation in autonomous driving environment Yang, Ziqin Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Engineering::Electrical and electronic engineering Depth estimation is one of the critical problems for autonomous vehicles (AVs) in 3D building modeling, SLAM, and localization. One solution is to leverage Radar and LiDAR to capture sparse point clouds, while the hardware is expensive for wide applications. Alternatively, deep learning (DL) networks have accomplished high performance in various vision tasks including image-based depth estimation, where only a camera is needed for image capturing. In this project, we investigate models to estimate depth using pure image information. We also conduct experiments to demonstrate the effectiveness and to evaluate the performance of these investigated models. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-27T07:28:21Z 2022-05-27T07:28:21Z 2022 Final Year Project (FYP) Yang, Z. (2022). Image-based depth estimation in autonomous driving environment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158161 https://hdl.handle.net/10356/158161 en A3248-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Yang, Ziqin Image-based depth estimation in autonomous driving environment |
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Depth estimation is one of the critical problems for autonomous vehicles (AVs) in 3D building modeling, SLAM, and localization. One solution is to leverage Radar and LiDAR to capture sparse point clouds, while the hardware is expensive for wide applications. Alternatively, deep learning (DL) networks have accomplished high performance in various vision tasks including image-based depth estimation, where only a camera is needed for image capturing. In this project, we investigate models to estimate depth using pure image information. We also conduct experiments to demonstrate the effectiveness and to evaluate the performance of these investigated models. |
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Tay Wee Peng |
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Tay Wee Peng Yang, Ziqin |
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Final Year Project |
author |
Yang, Ziqin |
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Yang, Ziqin |
title |
Image-based depth estimation in autonomous driving environment |
title_short |
Image-based depth estimation in autonomous driving environment |
title_full |
Image-based depth estimation in autonomous driving environment |
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Image-based depth estimation in autonomous driving environment |
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Image-based depth estimation in autonomous driving environment |
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image-based depth estimation in autonomous driving environment |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/158161 |
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