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。

OSP2B: 一阶段点至框架网络用于 3D 映像跟踪

OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking

Learning robust feature matching between the template and search area is
crucial for 3D Siamese tracking. The core of Siamese feature matching is how to
assign high feature similarity on the corresponding points between the template
and search area for precise object localization. In this paper, we propose a
novel point cloud registration-driven Siamese tracking framework, with the
intuition that spatially aligned corresponding points (via 3D registration)
tend to achieve consistent feature representations. Specifically, our method
consists of two modules, including a tracking-specific nonlocal registration
module and a registration-aided Sinkhorn template-feature aggregation module.
The registration module targets at the precise spatial alignment between the
template and search area. The tracking-specific spatial distance constraint is
proposed to refine the cross-attention weights in the nonlocal module for
discriminative feature learning. Then, we use the weighted SVD to compute the
rigid transformation between the template and search area, and align them to
achieve the desired spatially aligned corresponding points. For the feature
aggregation model, we formulate the feature matching between the transformed
template and search area as an optimal transport problem and utilize the
Sinkhorn optimization to search for the outlier-robust matching solution. Also,
a registration-aided spatial distance map is built to improve the matching
robustness in indistinguishable regions (e.g., smooth surface). Finally, guided
by the obtained feature matching map, we aggregate the target information from
the template into the search area to construct the target-specific feature,
which is then fed into a CenterPoint-like detection head for object
localization. Extensive experiments on KITTI, NuScenes and Waymo datasets
verify the effectiveness of our proposed method.

本文提出一种新的点云注册驱动的 Siamese 跟踪框架，包括追踪特定的非本地注册模块和注册辅助的 Sinkhorn 模板特征聚合模块，以实现在精确定位物体的同时提高跟踪鲁棒性。