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.

通过引入一种新颖的基于扩散的边界精化方法，利用激光雷达点环绕粗糙边界框的无域扩散模型来同时精化边界框的位置、尺寸和方向，克服了现有模型在应用于具有不同传感器设置或地理位置的领域时面临的性能不稳定问题，从而在不同数据集、目标类别和检测器上实现了显著的改进。