  Natural scene understanding is a challenging task, particularly when
encountering images of multiple objects that are partially occluded. This
obstacle is given rise by varying object ordering and positioning. Existing
scene understanding paradigms are able to parse only the visible parts,
resulting in incomplete and unstructured scene interpretation. In this paper,
we investigate the problem of scene de-occlusion, which aims to recover the
underlying occlusion ordering and complete the invisible parts of occluded
objects. We make the first attempt to address the problem through a novel and
unified framework that recovers hidden scene structures without ordering and
amodal annotations as supervisions. This is achieved via Partial Completion
Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of
object masks and contents, respectively, in a self-supervised manner. Based on
PCNet-M and PCNet-C, we devise a novel inference scheme to accomplish scene
de-occlusion, via progressive ordering recovery, amodal completion and content
completion. Extensive experiments on real-world scenes demonstrate the superior
performance of our approach to other alternatives. Remarkably, our approach
that is trained in a self-supervised manner achieves comparable results to
fully-supervised methods. The proposed scene de-occlusion framework benefits
many applications, including high-quality and controllable image manipulation
and scene recomposition (see Fig. 1), as well as the conversion of existing
modal mask annotations to amodal mask annotations.
