DCDet: Dynamic Cross-based 3D Object Detector

Shuai Liu, Boyang Li, Zhiyu Fang, Kai Huang

Abstract: 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 with different scales. To solve these issues, we introduce a \textit{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 \textit{rotation-weighted Intersection over Union} (RWIoU) metric to replace the widely used $L_1$ metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code is available at https://github.com/Say2L/DCDet.git.