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

Abstract

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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.