Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914708780482560 |
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| author | Jakab, Daniel Deegan, Brian Michael Sharma, Sushil Grua, Eoin Martino Horgan, Jonathan Ward, Enda Van De Ven, Pepijn Scanlan, Anthony Eising, Ciarán |
| author_facet | Jakab, Daniel Deegan, Brian Michael Sharma, Sushil Grua, Eoin Martino Horgan, Jonathan Ward, Enda Van De Ven, Pepijn Scanlan, Anthony Eising, Ciarán |
| contents | In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle's surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators' ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_12041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges Jakab, Daniel Deegan, Brian Michael Sharma, Sushil Grua, Eoin Martino Horgan, Jonathan Ward, Enda Van De Ven, Pepijn Scanlan, Anthony Eising, Ciarán Computer Vision and Pattern Recognition Image and Video Processing In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle's surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators' ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems. |
| title | Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2402.12041 |