Accuracy of fish-eye lens models.

By Ciarán Hughes, Patrick Denny, Edward Jones, Martin Glavin. 2010

133 citations recorded by the source corpus.

The majority of computer vision applications assumes that the camera adheres to the pinhole camera model. However, most optical systems will introduce undesirable effects. By far, the most evident of these effects is radial lensing, which is particularly noticeable in fish-eye camera systems, where the effect is relatively extreme. Several authors have developed models of fish-eye lenses that can be used to describe the fish-eye displacement. Our aim is to evaluate the accuracy of several of these models. Thus, we present a method by which the lens curve of a fish-eye camera can be extracted using well-founded assumptions and perspective methods. Several of the models from the literature are examined against this empirically derived curve.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Senthil Kumar Yogamani
2. Ganesh Sistu
3. Stefan Milz
4. Sumanth Chennupati
5. Karl Amende
6. Michal Uřičář
7. Martin Simon
8. Hazem Rashed

In Media Technology

1. Ciarán Hughes
2. Patrick Pérez
3. Senthil Kumar Yogamani

In Signal Processing

1. Ciarán Hughes
2. Patrick Pérez
3. Senthil Kumar Yogamani
4. Michal Uřičář

Citations in the corpus

• WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “More detailed analysis of accuracy of various projection models is discussed in [25].”
• Graph-Based Classification of Omnidirectional Images (2017, unknown): “The natural way to do it is to apply calibration techniques [5, 6, 7] to undistort images and then use standard computer vision algorithms.”