An Enhanced Unified Camera Model
Open accessAbstract
This paper describes a novel projection model based on the so-called unified projection model. The new model applies to catadioptric systems and wide-angle fish-eye cameras, it does not require additional mapping to model distortions, and it takes just two projection parameters more than a simple pinhole model to represent radial distortion (one parameter more than the unified model). Here, we provide a study of different mathematical aspects of the model, its application limits, and explicit closed-form inversion. The latter allows to apply all the notions of epipolar geometry with no difficulties. Also, we introduce a concept of projection surface, which is a useful notion to study and compare different projection models with radial distortion. Using developed software, several different lenses were calibrated using the proposed model, and in all cases, subpixel precision was achieved.
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Semantic segmentation networks for autonomous driving have been successfully trained directly on fisheye images, but due to absence of fisheye datasets, they use artificially warped images of Cityscapes for training and test on fisheye images; artificial images cannot increase the originally captured FOV.
“Semantic segmentation networks for autonomous driving [47] have been successfully trained directly on fisheye images in [12,45].Due to absence of fisheye datasets, they make use of artificially warped images of Cityscapes for training and testing was performed on fisheye images.However, the artificial images cannot increase the originally captured FOV.”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [12]
Cites
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A toolbox for easily calibrating omnidirectional cameras
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Epipolar Geometry for Central Catadioptric Cameras
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A generic fisheye camera model for robotic applications
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Self-citationOmnidirectional visual servoing from polar lines
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WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · see citing works
“As a comparison, to give the reader an understanding of how different models behave, Figure 3 shows the mapping function r(θ) for five different projection models, which are Polynomial, Rectilinear, Stereographic, UCM and eUCM.”
Method
- Authors
- Khomutenko, B., Garcia, G., & Martinet, P.
- Affiliations
- École Centrale de Nantes (FR)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2016-01
- Volume
- 1
- Issue
- 1
- Pages
- 137-144
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2249194483
- Cited by
- 58
- References
- 12
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 9 · 2023: 9 · 2022: 6 · 2021: 9 · 2020: 5 · 2019: 5 · 2018: 2 · 2017: 1 · 2016: 1
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- catadioptric systems, fisheye camera, radial distortion, projection model, epipolar geometry, projection surface, lens calibration, sub-pixel precision
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