Dominated point cloud-based 3D object detectors in autonomous driving
scenarios rely heavily on the huge amount of accurately labeled samples,
however, 3D annotation in the point cloud is extremely tedious, expensive and
time-consuming. To reduce the dependence on large supervision, semi-supervised
learning (SSL) based approaches have been proposed. The Pseudo-Labeling
methodology is commonly used for SSL frameworks, however, the low-quality
predictions from the teacher model have seriously limited its performance. In
this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D
object detection, by enhancing the teacher model to a proficient one with
several necessary designs. First, to improve the recall of pseudo labels, a
Spatialtemporal Ensemble (STE) module is proposed to generate sufficient seed
boxes. Second, to improve the precision of recalled boxes, a Clusteringbased
Box Voting (CBV) module is designed to get aggregated votes from the clustered
seed boxes. This also eliminates the necessity of sophisticated thresholds to
select pseudo labels. Furthermore, to reduce the negative influence of wrongly
pseudo-labeled samples during the training, a soft supervision signal is
proposed by considering Box-wise Contrastive Learning (BCL). The effectiveness
of our model is verified on both ONCE and Waymo datasets. For example, on ONCE,
our approach significantly improves the baseline by 9.51 mAP. Moreover, with
half annotations, our model outperforms the oracle model with full annotations
on Waymo.

本文提出了一种新的 Pseudo-Labeling 半监督三维目标检测框架，通过引入 STE 和 CBV 模块提高了教师模型的性能，同时提出了 BCL 软监督信号来减小错误样本对训练的负面影响。实验证明，在 ONCE 和 Waymo 数据集上，我们的方法显著提高了基线，同时在 Waymo 数据集上，只使用了一半的注释即可超过完全注释的 oracle 模型。