Packetized Pipelined Pillar Feature Net Accelerator for LiDAR 3D Object Detection

Engineering notes

By integrating voxelization and feature extraction into a pipelined architecture, the proposed accelerator significantly reduces the storage requirements for point cloud data and enhances the speed of feature extraction and pseudo-image generation.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Implementing LiDAR-based 3D object detection algorithms in practical autonomous driving situations presents a significant challenge. In current research algorithms, the inherent sparsity and randomness of point cloud data necessitate significant memory usage and frequent data read/write operations during preprocessing. Such demands are not well-suited for terminal devices with stringent real-time requirements and constrained resources. In this paper, we present a packetized processing Pillar Feature Net accelerator for LiDAR 3D object detection. By integrating voxelization and feature extraction into a pipelined architecture, the proposed accelerator significantly reduces the storage requirements for point cloud data and enhances the speed of feature extraction and pseudo-image generation. Experimental results indicate that the proposed method improves the computational throughput from point cloud data to pseudo-image generation by 1.2 times and eliminates the need for off-chip memory access during preprocessing.

Links and sources

• Official / arXiv page

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