MoPRU: Learning Motion Prediction and Recurrent Motion-State Update for 3D Single Object Tracking

Motion-centric methods for LiDAR-based 3D single-object tracking (3D SOT) estimate relative target motion (RTM) from consecutive point-cloud frames for current target localization. Historical tracking information is further exploited for temporal motion modeling. However, learning the motion update only for current localization overlooks its effect on the recurrent motion state used for subsequent motion prediction. To address this issue, we propose MoPRU, a 3D SOT framework that unifies the learning of motion prediction and recurrent motion-state update. MoPRU learns how current motion information should be integrated into the recurrent motion state, extending motion-update learning beyond current-frame localization. Specifically, MoPRU aggregates historical tracking features into a recurrent motion state and predicts a motion prior for the current frame. The motion prior and current RTM determine a shared motion update, which refines the current RTM for target localization and updates the recurrent motion state for subsequent motion prediction. This motion update is learned by comparing short-horizon tracking results under alternative current updates. MoPRU achieves Success/Precision scores of 73.8/91.0 on KITTI and 62.93/75.66 on NuScenes, and improves zero-shot KITTI-to-Waymo transfer from 47.5/63.6 to 50.3/68.3.