DCDet: Dynamic Cross-based 3D Object Detector
Shuai Liu Boyang Li Zhiyu Fang Kai Huang
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.