Monocular Object Instance Segmentation and Depth Ordering with CNNs
Ziyu Zhang,Alexander G. Schwing,Sanja Fidler,Raquel Urtasun +3 more
- 07 Dec 2015
- pp 2614-2622
TL;DR: In this article, a Markov Random Field (MRF) is proposed to predict instance-level segmentation and depth ordering from a single monocular image, where the instance ID encodes the depth ordering within image patches.
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Abstract: In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural nets and train them to directly predict instance-level segmentations where the instance ID encodes the depth ordering within image patches. To provide a coherent single explanation of an image we develop a Markov random field which takes as input the predictions of convolutional neural nets applied at overlapping patches of different resolutions, as well as the output of a connected component algorithm. It aims to predict accurate instance-level segmentation and depth ordering. We demonstrate the effectiveness of our approach on the challenging KITTI benchmark and show good performance on both tasks.
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Citations
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