Rdssd: 3D Single Stage Object Detector For Roadside Lidar Sensors

Roadside 3D object detection is crucial for vehicle infrastructure cooperation systems. Due to the distinctive placement of roadside LiDAR, the distribution patterns of roadside point clouds and vehicle-side point clouds differ. In roadside point clouds, the proportion of foreground points in each instance is lower, leading to a notable decline in accuracy when using current sampling methods because of an unguided down-sampling strategy. To address this issue, this paper proposes a point-based single-stage 3D object detector called RDSSD for 3D object detection in roadside scenes. The paper designs a class-guided sampling strategy to efficiently select foreground points associated with potential objects. Furthermore, a task-oriented candidate prediction approach is introduced to generate candidate points that accurately represent the local scene from sampled key points. The experimental results on the DAIR-V2X-I have demonstrated that our method achieves the best detection performance with minimal computational cost.