Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs

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 Krähenbühl 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.