  Scene context is a powerful constraint on the geometry of objects within the
scene in cases, such as surveillance, where the camera geometry is unknown and
image quality may be poor. In this paper, we describe a method for estimating
the pose of cars in a scene jointly with the ground plane that supports them.
We formulate this as a joint optimization that accounts for varying car shape
using a statistical atlas, and which simultaneously computes geometry and
internal camera parameters. We demonstrate that this method produces
significant improvements for car pose estimation, and we show that the
resulting 3D geometry, when computed over a video sequence, makes it possible
to improve on state of the art classification of car behavior. We also show
that introducing the planar constraint allows us to estimate camera focal
length in a reliable manner.
