Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
This work addresses the problem of reducing annotation costs for 3D scene understanding, representing an incremental improvement by integrating diffusion models into existing teacher-student frameworks.
The paper tackles the challenge of generating reliable pseudo-labels in semi-supervised 3D object detection by proposing Diffusion-SS3D, which uses a diffusion model to denoise corrupted object size and class distributions, achieving state-of-the-art performance on ScanNet and SUN RGB-D benchmarks.
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.