  The point cloud based 3D single object tracking has drawn increasing
attention. Although many breakthroughs have been achieved, we also reveal two
severe issues. By extensive analysis, we find the prediction manner of current
approaches is non-robust, i.e., exposing a misalignment gap between prediction
score and actually localization accuracy. Another issue is the sparse point
returns will damage the feature matching procedure of the SOT task. Based on
these insights, we introduce two novel modules, i.e., Adaptive Refine
Prediction (ARP) and Target Knowledge Transfer (TKT), to tackle them,
respectively. To this end, we first design a strong pipeline to extract
discriminative features and conduct the matching with the attention mechanism.
Then, ARP module is proposed to tackle the misalignment issue by aggregating
all predicted candidates with valuable clues. Finally, TKT module is designed
to effectively overcome incomplete point cloud due to sparse and occlusion
issues. We call our overall framework PCET. By conducting extensive experiments
on the KITTI and Waymo Open Dataset, our model achieves state-of-the-art
performance while maintaining a lower computational cost.
