DocumentCode
2719010
Title
A theory of multi-layer flat refractive geometry
Author
Agrawal, Amit ; Ramalingam, Srikumar ; Taguchi, Yuichi ; Chari, Visesh
fYear
2012
fDate
16-21 June 2012
Firstpage
3346
Lastpage
3353
Abstract
Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm [15] can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.
Keywords
calibration; cameras; computational geometry; light refraction; matrix algebra; pose estimation; refractive index; 12th degree equation; 2D-3D correspondences; 3D point projection computation; 4th degree equation; 5-point algorithm; AFP equations; analytical forward projection equations; axial camera; axis estimation; calibration; classical essential matrix computation; layers thicknesses; multilayer flat refractive geometry theory; multiple parallel flat refractive mediums; noise stability; nonlinear refinement; pose estimation; refracting layer orientation; refractive indices; reprojection error minimization; single flat refractive mediums; single layer system; two layer system; Calibration; Cameras; Equations; Estimation; Geometry; Mathematical model; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4673-1226-4
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2012.6248073
Filename
6248073
Link To Document