Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection
<|reference_start|>Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection: 3D object detection is essential for understanding 3D scenes. Contemporary techniques often require extensive annotated training data, yet obtaining point-wise annotations for point clouds is time-consuming and laborious. Recent developments in semi-supervised methods seek to mitigate this problem by employing a teacher-student framework to generate pseudo-labels for unlabeled point clouds. However, these pseudo-labels frequently suffer from insufficient diversity and inferior quality. To overcome these hurdles, we introduce an Agent-based Diffusion Model for Semi-supervised 3D Object Detection (Diff3DETR). Specifically, an agent-based object query generator is designed to produce object queries that effectively adapt to dynamic scenes while striking a balance between sampling locations and content embedding. Additionally, a box-aware denoising module utilizes the DDIM denoising process and the long-range attention in the transformer decoder to refine bounding boxes incrementally. Extensive experiments on ScanNet and SUN RGB-D datasets demonstrate that Diff3DETR outperforms state-of-the-art semi-supervised 3D object detection methods.<|reference_end|>
@article{deng2024diff3detr:agent-based,
    title={Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object
  Detection},
    author={Jiacheng Deng, Jiahao Lu and Tianzhu Zhang},
    journal={arXiv preprint arXiv:2408.00286},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00286},
    primaryClass={cs.CV}
}