Papers › ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. 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.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | VPQ | 63.1 | #1 of 8 | Archive leaderboard | report |
| Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | VPQ (stuff) | 73.0 | #1 of 8 | Archive leaderboard | report |
| Video Panoptic Segmentation | Cityscapes-VPS | VIP-Deeplab | VPQ (thing) | 49.5 | #1 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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