论文标题
点云上轻巧和无探测器的3D单对象跟踪器
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
论文作者
论文摘要
3D单一对象跟踪的最新作品将任务视为特定于目标的3D检测任务，其中通常使用现成的3D检测器进行跟踪。但是，执行准确的目标特异性检测是不平凡的，因为原始激光雷达扫描中对象的点云通常稀疏且不完整。在本文中，我们通过明确利用时间运动提示来解决此问题，并提出DMT，这是一个基于无探测器的3D跟踪网络DMT，该网络完全消除了复杂的3D检测器的使用情况，并且比以前的跟踪器更轻，更快，更准确。具体而言，首先引入运动预测模块以无点云的方式估算当前帧的潜在目标中心。然后，提出了一个明确的投票模块，以直接从估计的目标中心回归3D框。对Kitti和Nuscenes数据集进行的广泛实验表明，与无需应用任何复杂的3D检测器的最先进的方法比最先进的方法要比最先进的方法要比最先进的方法更快地实现更好的性能（比Nuscenes数据集提高了10％（比Nuscenes数据集提高了10％）和更快的跟踪速度（即72 fps）。我们的代码在\ url {https://github.com/jimmy-dq/dmt}发布
Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance (~10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at \url{https://github.com/jimmy-dq/DMT}
