{
  "annotation_id": "f16f8f12-f839-4821-b02e-ba4ceede61a0",
  "date_created": "2026-02-17T05:53:12.438000Z",
  "date_modified": "2026-02-17T05:53:12.438000Z",
  "file_hash": "0d80e6a760262038c0c0e8d4f7a723fdcd0572bb4d26a24638b124cc93db4ec0",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.07119",
    "authors": [
      "Taisuke Noguchi",
      "Takayuki Nishio",
      "Takuya Azumi"
    ],
    "categories": [
      "cs.DC",
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration",
    "url": "https://arxiv.org/abs/2601.07119",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "20071fa7-d589-4449-90cf-0cbc33f11b0b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}