Geometric Distributions for Catadioptric Sensor Design
Abstract
Catadioptric sensors are visual sensors that employ lenses (dioptrics) and mirrors (catoptrics). We present a general method of catadioptric sensor design for realizing prescribed projections. Our method makes use of geometric distributions in. 3-dimensional space, which are generalizations of vector fields. The main idea is this: if one desires a reflective surface that will image the world in a certain way, then this condition determines the orientation of the tangent planes to the surface. Analytically, this means that the surface will then be determined by a pair of partial differential equations, which may or may not have a common solution. We show how to check if a common solution exists. If no common solution exists, we describe a method for obtaining optimal approximate solutions in a least-squares sense. As an example application, we construct a mirror that will give a panoramic view of a scene without any digital unwarping.
Cite
Text
Hicks and Perline. "Geometric Distributions for Catadioptric Sensor Design." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990526Markdown
[Hicks and Perline. "Geometric Distributions for Catadioptric Sensor Design." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/hicks2001cvpr-geometric/) doi:10.1109/CVPR.2001.990526BibTeX
@inproceedings{hicks2001cvpr-geometric,
title = {{Geometric Distributions for Catadioptric Sensor Design}},
author = {Hicks, R. Andrew and Perline, Ronald K.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:584-589},
doi = {10.1109/CVPR.2001.990526},
url = {https://mlanthology.org/cvpr/2001/hicks2001cvpr-geometric/}
}