تقرير
Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
| العنوان: | Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection |
|---|---|
| المؤلفون: | Ho, Cheng-Ju, Tai, Chen-Hsuan, Lin, Yen-Yu, Yang, Ming-Hsuan, Tsai, Yi-Hsuan |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted in NeurIPS 2023. Code is available at https://github.com/luluho1208/Diffusion-SS3D |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.02966 |
| رقم الأكسشن: | edsarx.2312.02966 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |