  Depth-aware panoptic segmentation is an emerging topic in computer vision
which combines semantic and geometric understanding for more robust scene
interpretation. Recent works pursue unified frameworks to tackle this challenge
but mostly still treat it as two individual learning tasks, which limits their
potential for exploring cross-domain information. We propose a deeply unified
framework for depth-aware panoptic segmentation, which performs joint
segmentation and depth estimation both in a per-segment manner with identical
object queries. To narrow the gap between the two tasks, we further design a
geometric query enhancement method, which is able to integrate scene geometry
into object queries using latent representations. In addition, we propose a
bi-directional guidance learning approach to facilitate cross-task feature
learning by taking advantage of their mutual relations. Our method sets the new
state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS
and SemKITTI-DVPS datasets. Moreover, our guidance learning approach is shown
to deliver performance improvement even under incomplete supervision labels.
