Spherical Light Fields

Bernd Krolla, Maximilian Diebold, Bastian Goldlücke and Didier Stricker

In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.67

Abstract

A full view spherical camera exploits its extended field of view to map the complete environment onto a 2D image plane. Thus, with a single shot, it delivers a lot more information about the surroundings than one can gather with a normal perspective or plenoptic camera, which are commonly used in light field imaging. However, in contrast to a light field camera, a spherical camera does not capture directional information about the incident light, and thus a single shot from a spherical camera is not sufficient to reconstruct 3D scene geometry. In this paper, we introduce a method combining spherical imaging with the light field approach. To obtain 3D information with a spherical camera, we capture several independent spherical images by applying a constant vertical offset between the camera positions and combine the images in a Spherical Light Field (SLF). We can then compute disparity maps by structure tensor orientation analysis on epipolar plane images, which in this context are 2D cuts through the spherical light field with constant azimuth angle. This method competes with the acquisition range of laser scanners and allows for a fast and extensive recording of a given scene. We benchmark our approach by comparing disparity maps of ray-traced scenes against its ground truth. Further we provide disparity maps of real world datasets.

Session

Poster Session

Files

Extended Abstract (PDF, 1 page, 552K)
Paper (PDF, 12 pages, 3.8M)
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Bibtex File

Citation

Bernd Krolla, Maximilian Diebold, Bastian Goldlücke, and Didier Stricker. Spherical Light Fields. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.

BibTex

@inproceedings{BMVC.28.67
	title = {Spherical Light Fields},
	author = {Krolla, Bernd and Diebold, Maximilian and Goldlücke, Bastian and Stricker, Didier},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
	doi = { http://dx.doi.org/10.5244/C.28.67 }
}