Volume 30 Issue 5
Sep.  2021
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YUE Yuanchen, CAI Yunfei, WANG Dongsheng. GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud[J]. Chinese Journal of Electronics, 2021, 30(5): 931-939. doi: 10.1049/cje.2021.07.004
Citation: YUE Yuanchen, CAI Yunfei, WANG Dongsheng. GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud[J]. Chinese Journal of Electronics, 2021, 30(5): 931-939. doi: 10.1049/cje.2021.07.004

GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud

doi: 10.1049/cje.2021.07.004
Funds:

This work is supported by the Fund for Technical Field of the Foundation Strengthening Programme (No.2019-JCJQ-JJ-355).

  • Received Date: 2019-07-29
    Available Online: 2021-09-02
  • 3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performance caused by large numbers of 3D convolutions, a novel end-to-end real-time object detection algorithm named GridNet-3D is proposed. In the work, 2D gridmapping is used to preprocess the original point clouds. Then a novel structure grid encoding layer is adopted to encode point cloud features and is gotten grid feature maps in bird's eye view which is connected to region proposal network module to generate detections. Despite only using point clouds, the results on the KITTI 3D detection benchmark show that our algorithm has higher detection precision and better real-time performance on the detection of cars, pedestrians and cyclists, which has high practical value.
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