{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scene-centric-unsupervised-panoptic","title":"Scene-Centric Unsupervised Panoptic Segmentation","arxiv_id":"2504.01955","date":"2025-04-02","proceeding":"CVPR 2025 1","authors":["Oliver Hahn","Christoph Reich","Nikita Araslanov","Daniel Cremers","Christian Rupprecht","Stefan Roth"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2504.01955v1","url_pdf":"https://arxiv.org/pdf/2504.01955v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scene-centric-unsupervised-panoptic","repo_url":"https://github.com/visinf/cups","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-object-detection","task_name":"Unsupervised Object Detection"},{"task_slug":"unsupervised-panoptic-segmentation","task_name":"Unsupervised Panoptic Segmentation"},{"task_slug":"unsupervised-semantic-segmentation","task_name":"Unsupervised Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-bdd100k","task":"Unsupervised Panoptic Segmentation","dataset":"BDD100K val","model":"CUPS (40 pseudo-classes)","rank_in_archive_order":1,"of":4,"metrics":{"PQ":"21.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-bdd100k","task":"Unsupervised Panoptic Segmentation","dataset":"BDD100K val","model":"CUPS (54 pseudo-classes)","rank_in_archive_order":2,"of":4,"metrics":{"PQ":"21.8"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-bdd100k","task":"Unsupervised Panoptic Segmentation","dataset":"BDD100K val","model":"CUPS (27 pseudo-classes)","rank_in_archive_order":3,"of":4,"metrics":{"PQ":"19.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on","task":"Unsupervised Panoptic Segmentation","dataset":"Cityscapes","model":"CUPS (54 pseudo-classes)","rank_in_archive_order":1,"of":5,"metrics":{"PQ":"30.6"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on","task":"Unsupervised Panoptic Segmentation","dataset":"Cityscapes","model":"CUPS (40 pseudo-classes)","rank_in_archive_order":2,"of":5,"metrics":{"PQ":"30.3"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on","task":"Unsupervised Panoptic Segmentation","dataset":"Cityscapes","model":"CUPS (27 pseudo-classes)","rank_in_archive_order":3,"of":5,"metrics":{"PQ":"27.8"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-kitti","task":"Unsupervised Panoptic Segmentation","dataset":"KITTI","model":"CUPS (54 pseudo-classes)","rank_in_archive_order":1,"of":4,"metrics":{"PQ":"28.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-kitti","task":"Unsupervised Panoptic Segmentation","dataset":"KITTI","model":"CUPS (40 pseudo-classes)","rank_in_archive_order":2,"of":4,"metrics":{"PQ":"28.1"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-kitti","task":"Unsupervised Panoptic Segmentation","dataset":"KITTI","model":"CUPS (27 pseudo-classes)","rank_in_archive_order":3,"of":4,"metrics":{"PQ":"25.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-muses","task":"Unsupervised Panoptic Segmentation","dataset":"MUSES: MUlti-SEnsor Semantic perception dataset","model":"CUPS (40 pseudo-classes)","rank_in_archive_order":1,"of":4,"metrics":{"PQ":"28.2"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-muses","task":"Unsupervised Panoptic Segmentation","dataset":"MUSES: MUlti-SEnsor Semantic perception dataset","model":"CUPS (27 pseudo-classes)","rank_in_archive_order":2,"of":4,"metrics":{"PQ":"24.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-muses","task":"Unsupervised Panoptic Segmentation","dataset":"MUSES: MUlti-SEnsor Semantic perception dataset","model":"CUPS (54 pseudo-classes)","rank_in_archive_order":3,"of":4,"metrics":{"PQ":"22.8"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-waymo","task":"Unsupervised Panoptic Segmentation","dataset":"Waymo Open Dataset","model":"CUPS (54 pseudo-classes)","rank_in_archive_order":1,"of":4,"metrics":{"PQ":"27.3"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-waymo","task":"Unsupervised Panoptic Segmentation","dataset":"Waymo Open Dataset","model":"CUPS (40 pseudo-classes)","rank_in_archive_order":2,"of":4,"metrics":{"PQ":"27.2"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-panoptic-segmentation-on-waymo","task":"Unsupervised Panoptic Segmentation","dataset":"Waymo Open Dataset","model":"CUPS (27 pseudo-classes)","rank_in_archive_order":3,"of":4,"metrics":{"PQ":"26.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-semantic-segmentation-on","task":"Unsupervised Semantic Segmentation","dataset":"Cityscapes test","model":"CUPS","rank_in_archive_order":1,"of":14,"metrics":{"Accuracy":"83.2 ","mIoU":"26.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2504.01955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.01955"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/visinf/cups","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":8},"by_repo_kind":{"official":{"samples":8,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"60c827a00b5a152a","entry":"denormalize","repo":"visinf/cups","repo_kind":"official","path":"cups/utils.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"60c827a00b5a152a"}},{"code_sha256_prefix":"42ac537df132d369","entry":"get_default_config","repo":"visinf/cups","repo_kind":"official","path":"cups/config.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/config.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"42ac537df132d369"}},{"code_sha256_prefix":"413101e146fc43f2","entry":"get_label_efficient_augmentations","repo":"visinf/cups","repo_kind":"official","path":"cups/augmentation.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/augmentation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"413101e146fc43f2"}},{"code_sha256_prefix":"08b8b7e673e8f446","entry":"get_pseudo_label_augmentations","repo":"visinf/cups","repo_kind":"official","path":"cups/augmentation.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/augmentation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"08b8b7e673e8f446"}},{"code_sha256_prefix":"c9001204e49aa348","entry":"normalize","repo":"visinf/cups","repo_kind":"official","path":"cups/utils.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c9001204e49aa348"}},{"code_sha256_prefix":"2d2fde55e9fd49fe","entry":"normalize_min_max_m1_1","repo":"visinf/cups","repo_kind":"official","path":"cups/utils.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2d2fde55e9fd49fe"}},{"code_sha256_prefix":"abf1bf4d18504b03","entry":"prediction_to_standard_format","repo":"visinf/cups","repo_kind":"official","path":"cups/model/model.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/model/model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"abf1bf4d18504b03"}},{"code_sha256_prefix":"af00744bd55b9c34","entry":"soft_hamming_distance","repo":"visinf/cups","repo_kind":"official","path":"cups/scene_flow_2_se3/loss.py","file_url":"https://github.com/visinf/cups/blob/HEAD/cups/scene_flow_2_se3/loss.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"af00744bd55b9c34"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}