Evaluation and comparison of various deep neural networks for monocular depth estimation
In this final year project, several testing scenarios and related methodology have been designed to examine the performance of the cutting-edge neural networks for monocular depth estimation. Since neural networks for monocular depth estimation is a fast-developing and emerging research field in rec...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/136904 |
| _version_ | 1824456182047178752 |
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| author | Zhang, Ziyi |
| author2 | Wang Han |
| author_facet | Wang Han Zhang, Ziyi |
| author_sort | Zhang, Ziyi |
| collection | NTU |
| description | In this final year project, several testing scenarios and related methodology have been designed to examine the performance of the cutting-edge neural networks for monocular depth estimation. Since neural networks for monocular depth estimation is a fast-developing and emerging research field in recent years, neural network design and techniques involved keep evolving. It is both reasonable and beneficial to perceive different novel network design and implement these networks personally. If all the parameters during testing meet the lowest expectations in relative real-life application scenarios, it can be expected that neural networks will replace the dedicated depth sensors and make a huge difference in high-tech fields like artificial intelligence and autonomous driving. |
| first_indexed | 2025-02-19T03:50:02Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/136904 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:50:02Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1369042023-07-07T18:04:45Z Evaluation and comparison of various deep neural networks for monocular depth estimation Zhang, Ziyi Wang Han School of Electrical and Electronic Engineering hw@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems In this final year project, several testing scenarios and related methodology have been designed to examine the performance of the cutting-edge neural networks for monocular depth estimation. Since neural networks for monocular depth estimation is a fast-developing and emerging research field in recent years, neural network design and techniques involved keep evolving. It is both reasonable and beneficial to perceive different novel network design and implement these networks personally. If all the parameters during testing meet the lowest expectations in relative real-life application scenarios, it can be expected that neural networks will replace the dedicated depth sensors and make a huge difference in high-tech fields like artificial intelligence and autonomous driving. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-02-05T01:33:09Z 2020-02-05T01:33:09Z 2019 Final Year Project (FYP) https://hdl.handle.net/10356/136904 en A1247-182 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Zhang, Ziyi Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title | Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title_full | Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title_fullStr | Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title_full_unstemmed | Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title_short | Evaluation and comparison of various deep neural networks for monocular depth estimation |
| title_sort | evaluation and comparison of various deep neural networks for monocular depth estimation |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/136904 |
| work_keys_str_mv | AT zhangziyi evaluationandcomparisonofvariousdeepneuralnetworksformonoculardepthestimation |