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

Ziyu Zhang, Sanja Fidler, Raquel Urtasun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 669-677

Abstract


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 [Krahenbuhl 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].

Related Material


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[bibtex]
@InProceedings{Zhang_2016_CVPR,
author = {Zhang, Ziyu and Fidler, Sanja and Urtasun, Raquel},
title = {Instance-Level Segmentation for Autonomous Driving With Deep Densely Connected MRFs},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}