FROM 3D TO 4D: FIXING THE ERRONEOUS COUPLING BETWEEN IOU AND ANGLE FOR OPTIMIZING 3D OBJECT DETECTION

The IoU metric directly measures the overlap between two boxes, maintaining consistency in model optimization and testing stages. It has emerged as a highly regarded regression loss in the field of 3D object detection. However, the optimization of IoU often leads to an increased angular error. This erroneous coupling phenomenon renders the model susceptible to settling into sub-optimal solutions, which have not been extensively analyzed and addressed, significantly impeding further advancements in the accuracy of 3D object detection. In this paper, a novel concept "4DIoU" is introduced for 3D object detection, where the angle information is integrated as an additional dimension in the IoU calculation, and a new formula for measuring angle correlation is proposed. The 4DIoU not only resolves the erroneous coupling between IoU and angles but also capitalizes on angle information to enhance network optimization. Furthermore, a new encoding and decoding paradigm is proposed, which is more compatible with 4DIoU for object detection in point clouds. Extensive experiments on nuScenes, Waymo and KITTI datasets demonstrate the effectiveness of our method. The plug-and-play design of our approach proves to be highly versatile.