DCDet: Dynamic Cross-based 3D Object Detector
This work addresses label assignment inefficiencies in 3D object detection, offering improvements for autonomous driving applications, though it is incremental in nature.
The paper tackles the problem of insufficient and imbalanced positive samples in 3D object detection by introducing a dynamic cross label assignment scheme and a rotation-weighted IoU metric, achieving state-of-the-art results on benchmarks like KITTI and nuScenes.
Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects of varying scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region, thus providing sufficient and balanced positive samples for training. Furthermore, to address the challenge of accurately regressing objects with varying scales, we put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code will be available at https://github.com/Say2L/DCDet.git.