  Accurate and effective 3D object detection is critical for ensuring the
driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D
object detectors have exhibited promising performance. However, these methods
refine proposals individually, ignoring the rich contextual information in the
object relationships between the neighbor proposals. In this study, we
introduce an object relation module, consisting of a graph generator and a
graph neural network (GNN), to learn the spatial information from certain
patterns to improve 3D object detection. Specifically, we create an
inter-object relationship graph based on proposals in a frame via the graph
generator to connect each proposal with its neighbor proposals. Afterward, the
GNN module extracts edge features from the generated graph and iteratively
refines proposal features with the captured edge features. Ultimately, we
leverage the refined features as input to the detection head to obtain
detection results. Our approach improves upon the baseline PV-RCNN on the KITTI
validation set for the car class across easy, moderate, and hard difficulty
levels by 0.82%, 0.74%, and 0.58%, respectively. Additionally, our method
outperforms the baseline by more than 1% under the moderate and hard levels BEV
AP on the test server.
