  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.
