  Semi-supervised 3D object detection can benefit from the promising
pseudo-labeling technique when labeled data is limited. However, recent
approaches have overlooked the impact of noisy pseudo-labels during training,
despite efforts to enhance pseudo-label quality through confidence-based
filtering. In this paper, we examine the impact of noisy pseudo-labels on
IoU-based target assignment and propose the Reliable Student framework, which
incorporates two complementary approaches to mitigate errors. First, it
involves a class-aware target assignment strategy that reduces false negative
assignments in difficult classes. Second, it includes a reliability weighting
strategy that suppresses false positive assignment errors while also addressing
remaining false negatives from the first step. The reliability weights are
determined by querying the teacher network for confidence scores of the
student-generated proposals. Our work surpasses the previous state-of-the-art
on KITTI 3D object detection benchmark on point clouds in the semi-supervised
setting. On 1% labeled data, our approach achieves a 6.2% AP improvement for
the pedestrian class, despite having only 37 labeled samples available. The
improvements become significant for the 2% setting, achieving 6.0% AP and 5.7%
AP improvements for the pedestrian and cyclist classes, respectively.
