Good 3D object detection performance from LiDAR-Camera sensors demands
seamless feature alignment and fusion strategies. We propose the 3DifFusionDet
framework in this paper, which structures 3D object detection as a denoising
diffusion process from noisy 3D boxes to target boxes. In this framework,
ground truth boxes diffuse in a random distribution for training, and the model
learns to reverse the noising process. During inference, the model gradually
refines a set of boxes that were generated at random to the outcomes. Under the
feature align strategy, the progressive refinement method could make a
significant contribution to robust LiDAR-Camera fusion. The iterative
refinement process could also demonstrate great adaptability by applying the
framework to various detecting circumstances where varying levels of accuracy
and speed are required. Extensive experiments on KITTI, a benchmark for
real-world traffic object identification, revealed that 3DifFusionDet is able
to perform favorably in comparison to earlier, well-respected detectors.

该研究提出了 3DifFusionDet 框架，将 3D 目标检测视为从噪声 3D 框到目标框的去噪扩散过程，并通过特征对齐策略和渐进改进方法，在 LiDAR-Camera 融合中取得了显著贡献。实验证明，3DifFusionDet 在 KITTI 上的表现优于前期的经典检测器。