PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
This addresses vehicle detection for autonomous driving safety, but it appears incremental as it builds on existing two-stage detection pipelines with graph-based refinements.
The authors tackled 3D vehicle detection in autonomous driving by proposing PointRGCN, a graph-based pipeline using residual and contextual GCNs to refine LiDAR point cloud proposals, achieving state-of-the-art performance on the easy difficulty for bird's eye view detection.
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.