🤖 AI Summary
To address the high computational overhead, latency, and power consumption of LiDAR point cloud 3D object detection models on edge devices, this work pioneers the application of split computing to point cloud detection. We propose a dual-granularity collaborative inference framework targeting both voxelized representations and internal network features. By partitioning the model either after voxelization or at intermediate layers of the backbone network, our approach achieves balanced edge–cloud workload distribution and enables compression of sensitive features for transmission—preserving detection accuracy while substantially reducing edge-side burden. Experiments demonstrate that post-voxelization splitting reduces total inference time by 70.8% and edge execution time by 90.0%; internal-network splitting achieves up to 57.1% reduction in end-to-end latency and 69.5% reduction in edge computation time. This work establishes a deployable, real-time 3D perception paradigm for resource-constrained edge scenarios.
📝 Abstract
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%.