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Solving this problem requires the vision models to predict the spatial location, semantic class, and temporally consistent instance label for each 3D point. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public. On the individual sub-tasks, ViP-DeepLab also achieves state-of-the-art results, outperforming previous methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTI monocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian. The datasets and the evaluation codes are made publicly available.","url_abs":"https://arxiv.org/abs/2012.05258v1","url_pdf":"https://arxiv.org/pdf/2012.05258v1.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":"vip-deeplab-learning-visual-perception-with","repo_url":"https://github.com/joe-siyuan-qiao/ViP-DeepLab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"vip-deeplab-learning-visual-perception-with","repo_url":"https://github.com/MindSpore-scientific/code-7/tree/main/GroupViT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"depth-aware-video-panoptic-segmentation","task_name":"Depth-aware Video Panoptic Segmentation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"video-panoptic-segmentation","task_name":"Video Panoptic Segmentation"}],"methods":[{"method_slug":"vip-deeplab","method_name":"ViP-DeepLab"}],"datasets_introduced":[{"slug":"cityscapes-dvps","name":"Cityscapes-DVPS","full_name":""},{"slug":"semkitti-dvps","name":"SemKITTI-DVPS","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-panoptic-segmentation-on-cityscapes-vps","task":"Video Panoptic Segmentation","dataset":"Cityscapes-VPS","model":"VIP-Deeplab","rank_in_archive_order":1,"of":8,"metrics":{"VPQ":"63.1","VPQ (stuff)":"73.0","VPQ (thing)":"49.5"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2012.05258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.05258"}},"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. 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