  Ensuring robust 3D object detection and localization is crucial for many
applications in robotics and autonomous driving. Recent models, however, face
difficulties in maintaining high performance when applied to domains with
differing sensor setups or geographic locations, often resulting in poor
localization accuracy due to domain shift. To overcome this challenge, we
introduce a novel diffusion-based box refinement approach. This method employs
a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding
a coarse bounding box, to simultaneously refine the box's location, size, and
orientation. We evaluate this approach under various domain adaptation
settings, and our results reveal significant improvements across different
datasets, object classes and detectors.
