@string{eccv-cvrsuad="Proceedings of the ECCV Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving"}
@string{lncs="Lecture Notes in Computer Science"}
@string{springer="Springer"}
@INPROCEEDINGS{Tamas-etal2014,
  author =	 {Levente Tamas and Robert Frohlich and Zoltan Kato},
  title =	 {Relative Pose Estimation and Fusion of
                  Omnidirectional and Lidar Cameras},
  booktitle =	 eccv-cvrsuad,
  year =	 2014,
  series =	 lncs,
  address =	 {Zurich, Switzerland},
  month =	 sep,
  publisher =	 springer,
  pages =	 {640-651},
  editor =	 {Lourdes de Agapito and Michael M. Bronstein and
                  Carsten Rother},
  volume =	 8926,
  isbn =	 {ISBN 978-3-319-16180-8},
  abstract =	 {This paper presents a novel approach for the
                  extrinsic parameter estimation of omnidirectional
                  cameras with respect to a 3D Lidar coordinate
                  frame. 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. It relies on a set of corresponding
                  regions, and pose parameters are obtained by solving
                  a small system of nonlinear equations. The
                  efficiency and robustness of the proposed method was
                  confirmed on both synthetic and real data in urban
                  environment.},
}