  Semi-supervised learning aims to leverage numerous unlabeled data to improve
the model performance. Current semi-supervised 3D object detection methods
typically use a teacher to generate pseudo labels for a student, and the
quality of the pseudo labels is essential for the final performance. In this
paper, we propose PatchTeacher, which focuses on partial scene 3D object
detection to provide high-quality pseudo labels for the student. Specifically,
we divide a complete scene into a series of patches and feed them to our
PatchTeacher sequentially. PatchTeacher leverages the low memory consumption
advantage of partial scene detection to process point clouds with a
high-resolution voxelization, which can minimize the information loss of
quantization and extract more fine-grained features. However, it is non-trivial
to train a detector on fractions of the scene. Therefore, we introduce three
key techniques, i.e., Patch Normalizer, Quadrant Align, and Fovea Selection, to
improve the performance of PatchTeacher. Moreover, we devise PillarMix, a
strong data augmentation strategy that mixes truncated pillars from different
LiDAR scans to generate diverse training samples and thus help the model learn
more general representation. Extensive experiments conducted on Waymo and ONCE
datasets verify the effectiveness and superiority of our method and we achieve
new state-of-the-art results, surpassing existing methods by a large margin.
Codes are available at https://github.com/LittlePey/PTPM.
