Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
This addresses the problem of robust scene interpretation in computer vision by integrating semantic and geometric understanding, though it is incremental as it builds on existing unified frameworks.
The paper tackles depth-aware panoptic segmentation by proposing a deeply unified framework that jointly performs segmentation and depth estimation using identical object queries, achieving new state-of-the-art results on Cityscapes-DVPS and SemKITTI-DVPS datasets.
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.