用摄像头实现动态场景的4D全景占位追踪，精度领先
TrackOcc: Camera-based 4D Panoptic Occupancy Tracking
- 用4D全景查询流式处理图像，端到端完成占位分割与物体追踪
- 在Waymo数据集上达到当前最优性能，关键指标显著提升
- 适合自动驾驶感知系统研发人员参考，尤其关注时空一致性
仅基于摄像头输入的全面且一致的动态场景理解对高级自动驾驶系统至关重要。传统基于摄像头的感知任务如3D物体追踪和语义占位预测，或缺乏空间完整性，或缺少时间一致性。本文提出一项新任务——基于摄像头的4D全景占位追踪，同时解决全景占位分割与物体追踪问题。为此，我们提出TrackOcc方法，通过4D全景查询以流式、端到端方式处理图像输入。利用定位感知损失，该方法在不依赖复杂组件的情况下显著提升4D全景占位追踪精度。实验表明，TrackOcc在Waymo数据集上达到当前最优性能。源代码将发布于https://github.com/Tsinghua-MARS-Lab/TrackOcc。
原文摘要 · Abstract (English)
Comprehensive and consistent dynamic scene understanding from camera input is essential for advanced autonomous systems. Traditional camera-based perception tasks like 3D object tracking and semantic occupancy prediction lack either spatial comprehensiveness or temporal consistency. In this work, we introduce a brand-new task, Camera-based 4D Panoptic Occupancy Tracking, which simultaneously addresses panoptic occupancy segmentation and object tracking from camera-only input. Furthermore, we propose TrackOcc, a cutting-edge approach that processes image inputs in a streaming, end-to-end manner with 4D panoptic queries to address the proposed task. Leveraging the localization-aware loss, TrackOcc enhances the accuracy of 4D panoptic occupancy tracking without bells and whistles. Experimental results demonstrate that our method achieves state-of-the-art performance on the Waymo dataset. The source code will be released at https://github.com/Tsinghua-MARS-Lab/TrackOcc.
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