  3D object detection is an important task in computer vision. Most existing
methods require a large number of high-quality 3D annotations, which are
expensive to collect. Especially for outdoor scenes, the problem becomes more
severe due to the sparseness of the point cloud and the complexity of urban
scenes. Semi-supervised learning is a promising technique to mitigate the data
annotation issue. Inspired by this, we propose a novel semi-supervised
framework based on pseudo-labeling for outdoor 3D object detection tasks. We
design the Adaptive Class Confidence Selection module (ACCS) to generate
high-quality pseudo-labels. Besides, we propose Holistic Point Cloud
Augmentation (HPCA) for unlabeled data to improve robustness. Experiments on
the KITTI benchmark demonstrate the effectiveness of our method.
