title: Scene-Centric Unsupervised Panoptic Segmentation
summary: 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.
paper_authors._id: 67f00313579927adf0ee1d8b
paper_authors.name: Oliver Hahn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:53:34.223Z
paper_authors._id: 636020de88e41d249eccf68c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/636020de88e41d249eccf68c/pMduOM5UNZo4JW2Ckk2jK.jpeg
paper_authors.fullname: Oliver Hahn
paper_authors.type: user
paper_authors.user: olvrhhn
paper_authors._id: 67f00313579927adf0ee1d8c
paper_authors.name: Christoph Reich
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-06T08:11:31.401Z
paper_authors._id: 67f001a5f824e54a66563455
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AG6K_dOseLcc84tHP7V8D.png
paper_authors.fullname: Christoph Reich
paper_authors.type: user
paper_authors.user: ChristophReich1996
paper_authors._id: 67f00313579927adf0ee1d8d
paper_authors.name: Nikita Araslanov
paper_authors._id: 67f00313579927adf0ee1d8e
paper_authors.name: Daniel Cremers
paper_authors._id: 67f00313579927adf0ee1d8f
paper_authors.name: Christian Rupprecht
paper_authors._id: 67f00313579927adf0ee1d90
paper_authors.name: Stefan Roth
paper_title: Scene-Centric Unsupervised Panoptic Segmentation
paper_summary: 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.
paper_ai_summary: A novel unsupervised panoptic segmentation method combines visual representations, depth, and motion cues to achieve state-of-the-art performance on Cityscapes without human annotations.
paper_ai_keywords: unsupervised panoptic segmentation panoptic pseudo labels scene-centric imagery panoptic self-training strategy Cityscapes PQ