3D Point Cloud Object Detection on Edge Devices for Split Computing

• URL: http://arxiv.org/abs/2511.02293v1
• Date: Tue, 04 Nov 2025 06:15:24 GMT
• Title: 3D Point Cloud Object Detection on Edge Devices for Split Computing
• Authors: Taisuke Noguchi, Takuya Azumi,
• Abstract summary: Deep neural network models are complex, leading to longer processing times and increased power consumption on edge devices.<n>Split Computing aims to lessen the computational burden on edge devices, thereby reducing processing time and power consumption.<n> Experimental results show that splitting after voxelization reduces the inference time by 70.8% and the edge device execution time by 90.0%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

Related papers

• SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration [1.1761374316223123]
  3D object detection using LiDAR-based point cloud data and deep neural networks is essential in autonomous driving technology.<n> deploying state-of-the-art models on edge devices present challenges due to high computational demands and energy consumption.<n>This paper proposes SC-MII, multiple infrastructure LiDAR-based 3D object detection on edge devices for Split Computing with Multiple Intermediate outputs Integration.
  arXiv  Detail & Related papers  (2026-01-12T01:17:01Z)
• Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA [0.043533652831655174]
  We present a hardware implementation of an event-graph neural network for time-series classification.<n>We leverage an artificial cochlea model to convert the input time-series signals into a sparse event-data format.<n>Our method achieves a floating-point accuracy of 92.7% on the SHD dataset for the base model, which is only 2.4% and 2% less than the state-of-the-art models.
  arXiv  Detail & Related papers  (2025-03-09T14:08:46Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees [0.0]
  We propose an algorithm that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states. This bypasses the need to employ any relatively high latency deep-learning algorithms. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds.
  arXiv  Detail & Related papers  (2023-04-01T02:42:24Z)
• A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices [2.520804666686246]
  A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. We propose a lightweight concept drift detection method in cooperation with a recently proposed on-device learning technique of neural networks.
  arXiv  Detail & Related papers  (2022-12-19T17:13:59Z)
• Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras [62.70541164894224]
  We introduce several architecture choices which allow us to scale the depth and complexity of such models while maintaining low computation. Our method runs 3.7 times faster than a dense graph neural network, taking only 8.4 ms per forward pass.
  arXiv  Detail & Related papers  (2022-11-22T15:14:20Z)
• Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks [63.614519238823206]
  Spiking Neural Networks (SNNs) are a practical choice when the target hardware reaches the edge of computing. We employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error.
  arXiv  Detail & Related papers  (2022-11-22T15:08:05Z)
• NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
  This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction. The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
  arXiv  Detail & Related papers  (2022-09-29T04:06:00Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• ItNet: iterative neural networks with small graphs for accurate and efficient anytime prediction [1.52292571922932]
  In this study, we introduce a class of network models that have a small memory footprint in terms of their computational graphs. We show state-of-the-art results for semantic segmentation on the CamVid and Cityscapes datasets.
  arXiv  Detail & Related papers  (2021-01-21T15:56:29Z)
• Robust error bounds for quantised and pruned neural networks [1.8083503268672914]
  Machine learning algorithms are moving towards decentralisation with the data and algorithms stored, and even trained, locally on devices. The device hardware becomes the main bottleneck for model capability in this set-up, creating a need for slimmed down, more efficient neural networks. A semi-definite program is introduced to bound the worst-case error caused by pruning or quantising a neural network. It is hoped that the computed bounds will provide certainty to the performance of these algorithms when deployed on safety-critical systems.
  arXiv  Detail & Related papers  (2020-11-30T22:19:44Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.