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.

提出了一种简单而有效的一阶段点对框网络，该网络用于基于点云的 3D 单目标跟踪，其中采用中心感知注意力机制和二进制目标分类器等方法来提升跟踪效果。