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.

本文提出了一种基于运动中心范例的两阶段跟踪方法 M^2-Track 来解决传统外观匹配法在 LiDAR 点云跟踪中的不足，并在 KITTI、NuScenes 和 Waymo Open Dataset 三个数据集上表现出显著的精度提升（分别为 8％、17％和 22％），验证了该方法的有效性和前景。