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3D Object Detection

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    M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

    Garrick Brazil, Xiaoming Liu

    Understanding the world in 3D is a critical component of urban autonomous driving. Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount for successful 3D object detection algorithms, whereas monocular image-only methods experience drastically reduced performance. We propose to reduce the gap by reformulating the ...

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    Keywords: 3D Object Detection

  2. summary image

    Depth Coefficients for Depth Completion

    Saif Imran, Yunfei Long, Xiaoming Liu, Daniel Morris

    Depth completion involves estimating a dense depth image from sparse depth measurements, often guided by a color image. While linear upsampling is straight forward, it results in artifacts including depth pixels being interpolated in empty space across discontinuities between objects. Current methods use deep networks to upsample and "complete" the ...

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    Keywords: Depth Completion, 3D Object Detection

2019

  • M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
    Garrick Brazil, Xiaoming Liu
    In Proceeding of International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, Oct. 2019 (Oral presentation)
    Bibtex | PDF | arXiv | Project Webpage | Video
  • @inproceedings{ m3d-rpn-monocular-3d-region-proposal-network-for-object-detection,
      author = { Garrick Brazil and Xiaoming Liu },
      title = { M3D-RPN: Monocular 3D Region Proposal Network for Object Detection },
      booktitle = { In Proceeding of International Conference on Computer Vision },
      address = { Seoul, South Korea },
      month = { October },
      year = { 2019 },
    }