通过分割计算，显著降低边缘设备3D点云目标检测的耗时与功耗。
3D Point Cloud Object Detection on Edge Devices for Split Computing
- 将深度网络计算分拆到云端和边缘端，减轻边缘设备负担。
- 在体素化后分割可使边缘端执行时间减少90.0%。
- 适合资源受限的自动驾驶边缘设备部署，兼顾效率与数据安全。
自动驾驶技术快速发展，深度学习在感知领域起关键作用。特别是基于激光雷达采集的3D点云数据，需运行深度神经网络模型进行3D目标检测。然而，现有先进模型结构复杂，导致边缘设备处理时间长、功耗高。本文提出利用分割计算（Split Computing）这一分布式机器学习推理方法，缓解边缘设备的计算压力，从而降低处理时间和功耗。同时，仅传输网络中间特征，减少数据泄露风险。实验结果表明，于体素化后分割可使推理时间减少70.8%，边缘设备执行时间降低90.0%；若在网络内部分割，推理时间最多减少57.1%，边缘设备执行时间最多降低69.5%。
原文摘要 · Abstract (English)
The field of autonomous driving technology is rapidly advancing, with deep learning being a key component. Particularly in the field of sensing, 3D point cloud data collected by LiDAR is utilized to run deep neural network models for 3D object detection. However, these state-of-the-art models are complex, leading to longer processing times and increased power consumption on edge devices. The objective of this study is to address these issues by leveraging Split Computing, a distributed machine learning inference method. Split Computing aims to lessen the computational burden on edge devices, thereby reducing processing time and power consumption. Furthermore, it minimizes the risk of data breaches by only transmitting intermediate data from the deep neural network model. Experimental results show that splitting after voxelization reduces the inference time by 70.8% and the edge device execution time by 90.0%. When splitting within the network, the inference time is reduced by up to 57.1%, and the edge device execution time is reduced by up to 69.5%.
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