@phdthesis{Geiger2013,
  title = {Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms},
  abstract = {Visual 3D scene understanding is an important component in autonomous
   driving and robot navigation. Intelligent vehicles for example often
    base their decisions on observations obtained from video cameras
    as they are cheap and easy to employ. Inner-city intersections represent
    an interesting but also very challenging scenario in this context:
    The road layout may be very complex and observations are often noisy
    or even missing due to heavy occlusions. While Highway navigation
   and autonomous driving on simple and annotated intersections have
   already been demonstrated successfully, understanding and navigating
    general inner-city crossings with little prior knowledge remains
    an unsolved problem. This thesis is a contribution to understanding
   multi-object traffic scenes from video sequences. All data is provided
    by a camera system which is mounted on top of the autonomous driving
    platform AnnieWAY. The proposed probabilistic generative model reasons
    jointly about the 3D scene layout as well as the 3D location and
    orientation of objects in the scene. In particular, the scene topology,
   geometry as well as traffic activities are inferred from short video
    sequences. The model takes advantage of monocular information in
    the form of vehicle tracklets, vanishing lines and semantic labels.
   Additionally, the benefit of stereo features such as 3D scene flow
    and occupancy grids is investigated. Motivated by the impressive
    driving capabilities of humans, no further information such as GPS,
   lidar, radar or map knowledge is required. Experiments conducted
    on 113 representative intersection sequences show that the developed
    approach successfully infers the correct layout in a variety of difficult
   scenarios. To evaluate the importance of each feature cue, experiments
    with different feature combinations are conducted. Additionally,
    the proposed method is shown to improve object detection and object
   orientation estimation performance.},
  institution = {Karlsruhe Institute of Technology},
  school = {Karlsruhe Institute of Technology},
  month = apr,
  year = {2013},
  slug = {geiger2013},
  author = {Geiger, Andreas},
  month_numeric = {4}
}