  Our aim is to provide a pixel-wise instance-level labeling of a monocular
image in the context of autonomous driving. We build on recent work [Zhang et
al., ICCV15] that trained a convolutional neural net to predict instance
labeling in local image patches, extracted exhaustively in a stride from an
image. A simple Markov random field model using several heuristics was then
proposed in [Zhang et al., ICCV15] to derive a globally consistent instance
labeling of the image. In this paper, we formulate the global labeling problem
with a novel densely connected Markov random field and show how to encode
various intuitive potentials in a way that is amenable to efficient mean field
inference [Kr\"ahenb\"uhl et al., NIPS11]. Our potentials encode the
compatibility between the global labeling and the patch-level predictions,
contrast-sensitive smoothness as well as the fact that separate regions form
different instances. Our experiments on the challenging KITTI benchmark [Geiger
et al., CVPR12] demonstrate that our method achieves a significant performance
boost over the baseline [Zhang et al., ICCV15].
