FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds

@article{Zhou2019FVNet3F,
  title={FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds},
  author={Jie Zhou and Xuequan Lu and Xin Tan and Zhiwei Shao and Shouhong Ding and Lizhuang Ma},
  journal={2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)},
  year={2019},
  pages={1-8}
}
  • Jie Zhou, Xuequan Lu, +3 authors Lizhuang Ma
  • Published in
    12th International Congress…
    2019
  • Computer Science
  • Highlight Information
    3D object detection from raw and sparse point clouds has been far less treated to date, compared with its 2D counterpart. [...] Key Method Instead of generating proposals from camera images or bird's-eye-view maps, we first project point clouds onto a cylindrical surface to generate front-view feature maps which retains rich information. We then introduce a proposal generation network to predict 3D region proposals from the generated maps and further extrude objects of interest from the whole point cloud…Expand Abstract

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    Citations

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    3D Point Cloud Processing and Learning for Autonomous Driving

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    Deep Learning on Point Clouds and Its Application: A Survey

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