3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle tracking in unsupervised 2D SOT, we introduce the first semi-supervised approach to 3D SOT. Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy. Furthermore, we propose a data augmentation strategy named SOTMixup to improve the tracker's robustness to point cloud diversity. SOTMixup generates training samples by sampling points in two point clouds with a mixing rate and assigns a reasonable loss weight for training according to the mixing rate. The resulting MixCycle approach generalizes to appearance matching-based trackers. On the KITTI benchmark, based on the P2B tracker, MixCycle trained with $\textbf{10%}$ labels outperforms P2B trained with $\textbf{100%}$ labels, and achieves a $\textbf{28.4%}$ precision improvement when using $\textbf{1%}$ labels. Our code will be publicly released.

翻译：3D单目标跟踪是自动驾驶不可或缺的组成部分。现有方法严重依赖大规模密集标注数据集，但点云标注既昂贵又耗时。受无监督2D单目标跟踪中循环跟踪巨大成功的启发，我们首次提出面向3D单目标跟踪的半监督方法。具体而言，我们引入两种循环一致性策略用于监督：1）自跟踪循环，通过利用标签帮助模型在训练早期阶段更好收敛；2）前向-后向循环，增强跟踪器对运动变化及模板更新策略导致模板噪声的鲁棒性。此外，我们提出名为SOTMixup的数据增强策略，通过混合率对两个点云进行点采样生成训练样本，并依据混合率为训练分配合理损失权重，从而提升跟踪器对点云多样性的鲁棒性。所提出的MixCycle方法可泛化至基于外观匹配的跟踪器。在KITTI基准测试中，基于P2B跟踪器，使用$\textbf{10%}$标签训练的MixCycle性能优于使用$\textbf{100%}$标签训练的P2B，且在使用$\textbf{1%}$标签时实现$\textbf{28.4%}$的精度提升。我们的代码将公开开源。