  Amodal panoptic segmentation aims to connect the perception of the world to
its cognitive understanding. It entails simultaneously predicting the semantic
labels of visible scene regions and the entire shape of traffic participant
instances, including regions that may be occluded. In this work, we formulate a
proposal-free framework that tackles this task as a multi-label and multi-class
problem by first assigning the amodal masks to different layers according to
their relative occlusion order and then employing amodal instance regression on
each layer independently while learning background semantics. We propose the
\net architecture that incorporates a shared backbone and an asymmetrical
dual-decoder consisting of several modules to facilitate within-scale and
cross-scale feature aggregations, bilateral feature propagation between
decoders, and integration of global instance-level and local pixel-level
occlusion reasoning. Further, we propose the amodal mask refiner that resolves
the ambiguity in complex occlusion scenarios by explicitly leveraging the
embedding of unoccluded instance masks. Extensive evaluation on the BDD100K-APS
and KITTI-360-APS datasets demonstrate that our approach set the new
state-of-the-art on both benchmarks.
