Since Intersection-over-Union (IoU) based optimization maintains the
consistency of the final IoU prediction metric and losses, it has been widely
used in both regression and classification branches of single-stage 2D object
detectors. Recently, several 3D object detection methods adopt IoU-based
optimization and directly replace the 2D IoU with 3D IoU. However, such a
direct computation in 3D is very costly due to the complex implementation and
inefficient backward operations. Moreover, 3D IoU-based optimization is
sub-optimal as it is sensitive to rotation and thus can cause training
instability and detection performance deterioration. In this paper, we propose
a novel Rotation-Decoupled IoU (RDIoU) method that can mitigate the
rotation-sensitivity issue, and produce more efficient optimization objectives
compared with 3D IoU during the training stage. Specifically, our RDIoU
simplifies the complex interactions of regression parameters by decoupling the
rotation variable as an independent term, yet preserving the geometry of 3D
IoU. By incorporating RDIoU into both the regression and classification
branches, the network is encouraged to learn more precise bounding boxes and
concurrently overcome the misalignment issue between classification and
regression. Extensive experiments on the benchmark KITTI and Waymo Open Dataset
validate that our RDIoU method can bring substantial improvement for the
single-stage 3D object detection.

本文提出一种新的旋转解耦 IoU (RDIoU) 方法，通过将旋转变量作为独立项来简化回归参数的复杂交互，并在回归和分类分支中应用 RDIoU，从而可以提高单级 3D 目标检测的准确性和稳定性，并在 KITTI 和 Waymo 开放数据集的广泛实验中验证，此方法可以带来相当大的改进。