  In autonomous driving pipelines, perception modules provide a visual
understanding of the surrounding road scene. Among the perception tasks,
vehicle detection is of paramount importance for a safe driving as it
identifies the position of other agents sharing the road. In our work, we
propose PointRGCN: a graph-based 3D object detection pipeline based on graph
convolutional networks (GCNs) which operates exclusively on 3D LiDAR point
clouds. To perform more accurate 3D object detection, we leverage a graph
representation that performs proposal feature and context aggregation. We
integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D
object proposals are refined using a novel graph representation. In particular,
R-GCN is a residual GCN that classifies and regresses 3D proposals, and C-GCN
is a contextual GCN that further refines proposals by sharing contextual
information between multiple proposals. We integrate our refinement modules
into a novel 3D detection pipeline, PointRGCN, and achieve state-of-the-art
performance on the easy difficulty for the bird eye view detection task.
