DVPSFormer: 自動運転のための効率的なオンライン深度対応ビデオパノプティックセグメンテーション
DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving
自動運転向けに、深度・セマンティックセグメンテーション・インスタンス追跡を統合したオンライン4Dシーン理解アーキテクチャを提案。明示的なシーン離散化とオンライン多数決により、リアルタイム処理を実現し、ベンチマークでSOTAを達成。
著者: Yung-Hsu Yang, Luigi Piccinelli, Siyuan Li, Mattia Segu, Lei Ke, Martin Danelljan, Yuqian Fu, Zuria Bauer, Fisher Yu, Hermann Blum, Marc Pollefeys
分類: cs.CV
原文アブストラクト
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 https://royyang0714.github.io/DVPSFormer.