  Retrieving occlusion relation among objects in a single image is challenging
due to sparsity of boundaries in image. We observe two key issues in existing
works: firstly, lack of an architecture which can exploit the limited amount of
coupling in the decoder stage between the two subtasks, namely occlusion
boundary extraction and occlusion orientation prediction, and secondly,
improper representation of occlusion orientation. In this paper, we propose a
novel architecture called Occlusion-shared and Path-separated Network (OPNet),
which solves the first issue by exploiting rich occlusion cues in shared
high-level features and structured spatial information in task-specific
low-level features. We then design a simple but effective orthogonal occlusion
representation (OOR) to tackle the second issue. Our method surpasses the
state-of-the-art methods by 6.1%/8.3% Boundary-AP and 6.5%/10% Orientation-AP
on standard PIOD/BSDS ownership datasets. Code is available at
https://github.com/fengpanhe/MT-ORL.
