  In this paper we present a novel slanted-plane MRF model which reasons
jointly about occlusion boundaries as well as depth. We formulate the problem
as the one of inference in a hybrid MRF composed of both continuous (i.e.,
slanted 3D planes) and discrete (i.e., occlusion boundaries) random variables.
This allows us to define potentials encoding the ownership of the pixels that
compose the boundary between segments, as well as potentials encoding which
junctions are physically possible. Our approach outperforms the
state-of-the-art on Middlebury high resolution imagery as well as in the more
challenging KITTI dataset, while being more efficient than existing slanted
plane MRF-based methods, taking on average 2 minutes to perform inference on
high resolution imagery.
