  Recently, 3D object detection has attracted significant attention and
achieved continuous improvement in real road scenarios. The environmental
information is collected from a single sensor or multi-sensor fusion to detect
interested objects. However, most of the current 3D object detection approaches
focus on developing advanced network architectures to improve the detection
precision of the object rather than considering the dynamic driving scenes,
where data collected from sensors equipped in the vehicle contain various
perturbation features. As a result, existing work cannot still tackle the
perturbation issue. In order to solve this problem, we propose a group
equivariant bird's eye view network (GeqBevNet) based on the group equivariant
theory, which introduces the concept of group equivariant into the BEV fusion
object detection network. The group equivariant network is embedded into the
fused BEV feature map to facilitate the BEV-level rotational equivariant
feature extraction, thus leading to lower average orientation error. In order
to demonstrate the effectiveness of the GeqBevNet, the network is verified on
the nuScenes validation dataset in which mAOE can be decreased to 0.325.
Experimental results demonstrate that GeqBevNet can extract more rotational
equivariant features in the 3D object detection of the actual road scene and
improve the performance of object orientation prediction.
