@string{lncs="Lecture Notes in Computer Science"}
@string{springer="Springer"}
@InProceedings{Kato-Tamas2015,
  author =	 {Zoltan Kato and Levente Tamas},
  title =	 {Relative Pose Estimation and Fusion of 2D Spectral
                  and 3D Lidar Images},
  booktitle =	 {Proceedings of the Computational Color Imaging
                  Workshop},
  pages =	 {33-42},
  year =	 2015,
  editor =	 {Alain Tr\'emeau and Raimondo Schettini and Shoji
                  Tominaga:},
  volume =	 9016,
  series =	 lncs,
  address =	 {Saint-Etienne, France},
  month =	 mar,
  publisher =	 springer,
  abstract =	 {This paper presents a unified approach for the
                  relative pose estimation of a spectral camera - 3D
                  Lidar pair without the use of any special
                  calibration pattern or explicit point
                  correspondence. The method works without specific
                  setup and calibration targets, using only a pair of
                  2D-3D data. Pose estimation is formulated as a 2D-3D
                  nonlinear shape registration task which is solved
                  without point correspondences or complex similarity
                  metrics. The registration is then traced back to the
                  solution of a non-linear system of equations which
                  directly provides the calibration parameters between
                  the bases of the two sensors. The method has been
                  extended both for perspective and omnidirectional
                  central cameras and was tested on a large set of
                  synthetic lidar-camera image pairs as well as on
                  real data acquired in outdoor environment.}
}