3DifFusionDet: Diffusion Model for 3D Object Detection with Robust
  LiDAR-Camera Fusion
Xinhao Xiang Simon Dräger Jiawei Zhang
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.