3D object detection using LiDAR-based point cloud data and deep neural networks is essential in autonomous driving technology. However, deploying state-of-the-art models on edge devices present challenges due to high computational demands and energy consumption. Additionally, single LiDAR setups suffer from blind spots. This paper proposes SC-MII, multiple infrastructure LiDAR-based 3D object detection on edge devices for Split Computing with Multiple Intermediate outputs Integration. In SC-MII, edge devices process local point clouds through the initial DNN layers and send intermediate outputs to an edge server. The server integrates these features and completes inference, reducing both latency and device load while improving privacy. Experimental results on a real-world dataset show a 2.19x speed-up and a 71.6% reduction in edge device processing time, with at most a 1.09% drop in accuracy.

翻译：基于激光雷达点云数据与深度神经网络的3D目标检测是自动驾驶技术的关键环节。然而，由于计算需求高、能耗大，在边缘设备上部署先进模型面临诸多挑战。此外，单一激光雷达配置存在视野盲区问题。本文提出SC-MII，一种面向边缘设备、基于多路基础设施激光雷达的3D目标检测方法，采用多中间输出集成的分割计算架构。在SC-MII中，边缘设备通过初始DNN层处理局部点云，并将中间输出发送至边缘服务器。服务器集成这些特征并完成推理，在降低延迟与设备负载的同时提升了隐私保护能力。在真实数据集上的实验表明，该方法实现了2.19倍的加速效果，边缘设备处理时间减少71.6%，而精度损失最高仅为1.09%。