  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 released at
\url{https://github.com/Mumuqiao/MixCycle}.
