Affiliations: [a]
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| [b]
Institute of Coding (IoC), School of Computing, Newcastle University, Newcastle upon Tyne, NE17RU, UK
| [c]
School of Engineering, Newcastle University, Newcastle upon Tyne, NE17RU, UK
Correspondence:
[*]
Corresponding author. Haofeng Zhang, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. E-mail: zhanghf@njust.edu.cn.
Abstract: The ubiquitous availability of cost-effective cameras has rendered large scale collection of street view data a straightforward endeavour. Yet, the effective use of these data to assist autonomous driving remains a challenge, especially lack of exploration and exploitation of stereo images with abundant perceptible depth. In this paper, we propose a novel Depth-embedded Instance Segmentation Network (DISNet) which can effectively improve the performance of instance segmentation by incorporating the depth information of stereo images. The proposed network takes binocular images as input to observe the displacement of the object and estimate the corresponding depth perception without additional supervisions. Furthermore, we introduce a new module for computing the depth cost-volume, which can be integrated with the colour cost-volume to jointly capture useful disparities of stereo images. The shared-weights structure of Siamese Network is applied to learn the intrinsic information of stereo images while reducing the computational burden. Extensive experiments have been carried out on publicly available datasets (i.e., Cityscapes and KITTI), and the obtained results clearly demonstrate the superiority in segmenting instances with different depths.
Keywords: Instance segmentation, depth embedding, scene parsing
