  Two-stage point-to-box network acts as a critical role in the recent popular
3D Siamese tracking paradigm, which first generates proposals and then predicts
corresponding proposal-wise scores. However, such a network suffers from
tedious hyper-parameter tuning and task misalignment, limiting the tracking
performance. Towards these concerns, we propose a simple yet effective
one-stage point-to-box network for point cloud-based 3D single object tracking.
It synchronizes 3D proposal generation and center-ness score prediction by a
parallel predictor without tedious hyper-parameters. To guide a task-aligned
score ranking of proposals, a center-aware focal loss is proposed to supervise
the training of the center-ness branch, which enhances the network's
discriminative ability to distinguish proposals of different quality. Besides,
we design a binary target classifier to identify target-relevant points. By
integrating the derived classification scores with the center-ness scores, the
resulting network can effectively suppress interference proposals and further
mitigate task misalignment. Finally, we present a novel one-stage Siamese
tracker OSP2B equipped with the designed network. Extensive experiments on
challenging benchmarks including KITTI and Waymo SOT Dataset show that our
OSP2B achieves leading performance with a considerable real-time speed.Code
will be available at https://github.com/haooozi/OSP2B.
