DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
DVPSFormer is a unified online architecture for efficient 4D scene understanding, featuring explicit scene discretization (ESD) and a discrete-to-continuous (D2C) depth head for single-pass metric depth decoding, along with an online majority voting (OMV) mechanism for temporal consistency in instance tracking. It achieves state-of-the-art results on Cityscapes-DVPS and SemKITTI-DVPS benchmarks, providing a streamlined solution for online robotic perception.
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[Submitted on 28 Jul 2026]
Title:DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
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Abstract:Safe autonomous navigation requires a holistic understanding of dynamic environments, necessitating the simultaneous estimation of metric depth, semantic segmentation, and instance trajectories. While depth-aware video panoptic segmentation (DVPS) unifies these tasks, existing approaches often rely on computationally expensive, multi-stage pipelines or offline tracking, rendering them unsuitable for real-time decision-making. To address this, we propose DVPSFormer, a unified online architecture designed for efficient 4D scene understanding. Central to our approach is explicit scene discretization (ESD), a novel mechanism that leverages segmentation queries to represent foreground and background regions, enabling a discrete-to-continuous (D2C) depth head to decode metric depth in a single pass. This tightly couples semantic and geometric learning while significantly reducing latency. Furthermore, we propose an online majority voting (OMV) mechanism that exploits temporal consistency to refine classification during instance tracking. DVPSFormer establishes a new state-of-the-art on the Cityscapes-DVPS and SemKITTI-DVPS benchmarks, offering a streamlined solution for online robotic perception. Code and models are available at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.26165 [cs.CV]
(or arXiv:2607.26165v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26165
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yung-Hsu Yang [view email] [v1] Tue, 28 Jul 2026 18:16:00 UTC (5,028 KB)
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