Towards broader spatial-context 3D object detection for autonomous driving

This work presents an exhaustive analysis and a quantitative performance comparison between the use of information from infrastructure and vehicle mounted sensors for 3D object detection in autonomous driving environments. To do so, LiDAR point clouds have been used as the main data input and the most popular and well-established models have been considered for this task: Second, PointPillars and PV-RCNN. They have all been trained on the DAIR-V2X cooperative dataset, since it offers both the infrastructure and vehicle perspective. The broader spatial context and greater field of vision from an elevated point of view demonstrate superior performance by mitigating occlusions and overcoming the inherent limitations of a reduced perception range from onboard a vehicle. However, this comes with its own challenges to avoid losing detection capabilities for smaller objects. The main objective of this work is to provide a like-for-like comparison of the real performance difference, isolating the point of view as the only modified variable.