Monocular Object Instance Segmentation and Depth Ordering With CNNs

Ziyu Zhang, Alexander G. Schwing, Sanja Fidler, Raquel Urtasun; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2614-2622

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.