  Semi-supervised object detection is crucial for 3D scene understanding,
efficiently addressing the limitation of acquiring large-scale 3D bounding box
annotations. Existing methods typically employ a teacher-student framework with
pseudo-labeling to leverage unlabeled point clouds. However, producing reliable
pseudo-labels in a diverse 3D space still remains challenging. In this work, we
propose Diffusion-SS3D, a new perspective of enhancing the quality of
pseudo-labels via the diffusion model for semi-supervised 3D object detection.
Specifically, we include noises to produce corrupted 3D object size and class
label distributions, and then utilize the diffusion model as a denoising
process to obtain bounding box outputs. Moreover, we integrate the diffusion
model into the teacher-student framework, so that the denoised bounding boxes
can be used to improve pseudo-label generation, as well as the entire
semi-supervised learning process. We conduct experiments on the ScanNet and SUN
RGB-D benchmark datasets to demonstrate that our approach achieves
state-of-the-art performance against existing methods. We also present
extensive analysis to understand how our diffusion model design affects
performance in semi-supervised learning.
