1 research outputs found
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
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 labels outperforms P2B trained with
labels, and achieves a precision improvement when using
labels. Our code will be released at
\url{https://github.com/Mumuqiao/MixCycle}.Comment: Accepted by ICCV2