  Recovering the occlusion relationships between objects is a fundamental human
visual ability which yields important information about the 3D world. In this
paper we propose a deep network architecture, called DOC, which acts on a
single image, detects object boundaries and estimates the border ownership
(i.e. which side of the boundary is foreground and which is background). We
represent occlusion relations by a binary edge map, to indicate the object
boundary, and an occlusion orientation variable which is tangential to the
boundary and whose direction specifies border ownership by a left-hand rule. We
train two related deep convolutional neural networks, called DOC, which exploit
local and non-local image cues to estimate this representation and hence
recover occlusion relations. In order to train and test DOC we construct a
large-scale instance occlusion boundary dataset using PASCAL VOC images, which
we call the PASCAL instance occlusion dataset (PIOD). This contains 10,000
images and hence is two orders of magnitude larger than existing occlusion
datasets for outdoor images. We test two variants of DOC on PIOD and on the
BSDS occlusion dataset and show they outperform state-of-the-art methods.
Finally, we perform numerous experiments investigating multiple settings of DOC
and transfer between BSDS and PIOD, which provides more insights for further
study of occlusion estimation.
