  In this paper, we present a simple yet effective semi-supervised 3D object
detector named DDS3D. Our main contributions have two-fold. On the one hand,
different from previous works using Non-Maximal Suppression (NMS) or its
variants for obtaining the sparse pseudo labels, we propose a dense
pseudo-label generation strategy to get dense pseudo-labels, which can retain
more potential supervision information for the student network. On the other
hand, instead of traditional fixed thresholds, we propose a dynamic threshold
manner to generate pseudo-labels, which can guarantee the quality and quantity
of pseudo-labels during the whole training process. Benefiting from these two
components, our DDS3D outperforms the state-of-the-art semi-supervised 3d
object detection with mAP of 3.1% on the pedestrian and 2.1% on the cyclist
under the same configuration of 1% samples. Extensive ablation studies on the
KITTI dataset demonstrate the effectiveness of our DDS3D. The code and models
will be made publicly available at https://github.com/hust-jy/DDS3D
