论文标题
超越3D暹罗跟踪：一个以运动为中心的3D单一对象跟踪的范例
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
论文作者
论文摘要
LIDAR点云中的3D单一对象跟踪（3D SOT）在自主驾驶中起着至关重要的作用。当前方法都根据外观匹配遵循暹罗范式。但是，激光点云通常是无纹理和不完整的，这会阻碍有效的外观匹配。此外，以前的方法极大地忽略了目标之间的关键运动线索。在这项工作中，超越3D暹罗跟踪，我们引入了一个以运动为中心的范式来从新的角度处理3D SOT。在此范式之后，我们提出了一个无匹配的两阶段跟踪器M^2轨道。在1^ST阶段，M^2轨道通过运动转换将目标定位在连续的帧中。然后，它通过在2^nd阶段的运动辅助形状完成来完善目标框。广泛的实验证实，M^2 Track在三个大规模数据集上的先前最先前的表现明显优于以57fps运行（〜8％，〜17％和〜22％）的精度增长，分别为Kitti，Nuscenes和Waymo打开数据集）。进一步的分析验证了每个组件的有效性，并显示了以运动为中心的范式与外观匹配时的有希望的潜力。
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M^2-Track. At the 1^st-stage, M^2-Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2^nd-stage. Extensive experiments confirm that M^2-Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS (~8%, ~17%, and ~22%) precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). Further analysis verifies each component's effectiveness and shows the motion-centric paradigm's promising potential when combined with appearance matching.
