  Occlusion relationship reasoning based on convolution neural networks
consists of two subtasks: occlusion boundary extraction and occlusion
orientation inference. Due to the essential differences between the two
subtasks in the feature expression at the higher and lower stages, it is
challenging to carry on them simultaneously in one network. To address this
issue, we propose a novel Dual-path Decoder Network, which uniformly extracts
occlusion information at higher stages and separates into two paths to recover
boundary and occlusion orientation respectively in lower stages. Besides,
considering the restriction of occlusion orientation presentation to occlusion
orientation learning, we design a new orthogonal representation for occlusion
orientation and proposed the Orthogonal Orientation Regression loss which can
get rid of the unfitness between occlusion representation and learning and
further prompt the occlusion orientation learning. Finally, we apply a
multi-scale loss together with our proposed orientation regression loss to
guide the boundary and orientation path learning respectively. Experiments
demonstrate that our proposed method achieves state-of-the-art results on PIOD
and BSDS ownership datasets.
