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

Object detection using single point supervision has received increasing
attention over the years. However, the performance gap between point supervised
object detection (PSOD) and bounding box supervised detection remains large. In
this paper, we attribute such a large performance gap to the failure of
generating high-quality proposal bags which are crucial for multiple instance
learning (MIL). To address this problem, we introduce a lightweight alternative
to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box
Network (P2BNet), which can construct an inter-objects balanced proposal bag by
generating proposals in an anchor-like way. By fully investigating the accurate
position information, P2BNet further constructs an instance-level bag, avoiding
the mixture of multiple objects. Finally, a coarse-to-fine policy in a cascade
fashion is utilized to improve the IoU between proposals and ground-truth (GT).
Benefiting from these strategies, P2BNet is able to produce high-quality
instance-level bags for object detection. P2BNet improves the mean average
precision (AP) by more than 50% relative to the previous best PSOD method on
the MS COCO dataset. It also demonstrates the great potential to bridge the
performance gap between point supervised and bounding-box supervised detectors.
The code will be released at github.com/ucas-vg/P2BNet.

本文提出了 Point-to-Box Network (P2BNet) 方法，通过生成锚点式的建议来构建平衡的物体提议包，进而解决了单点监督目标检测方法存在的低性能问题。使用级联技术进一步提高了模型在 MS COCO 数据集上的表现。