Unsupervised panoptic segmentation aims to partition an image into
semantically meaningful regions and distinct object instances without training
on manually annotated data. In contrast to prior work on unsupervised panoptic
scene understanding, we eliminate the need for object-centric training data,
enabling the unsupervised understanding of complex scenes. To that end, we
present the first unsupervised panoptic method that directly trains on
scene-centric imagery. In particular, we propose an approach to obtain
high-resolution panoptic pseudo labels on complex scene-centric data, combining
visual representations, depth, and motion cues. Utilizing both pseudo-label
training and a panoptic self-training strategy yields a novel approach that
accurately predicts panoptic segmentation of complex scenes without requiring
any human annotations. Our approach significantly improves panoptic quality,
e.g., surpassing the recent state of the art in unsupervised panoptic
segmentation on Cityscapes by 9.4% points in PQ.
