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3D Object Detection

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    Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image

    Feng Liu, Xiaoming Liu

    Inferring 3D locations and shapes of multiple objects from a single 2D image is a long-standing objective of computer vision. Most of the existing works either predict one of these 3D properties or focus on solving both for a single object. One fundamental challenge lies in how to learn an ...

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    Keywords: 3D Object Detection, 3D Shape Reconstruction, Generic Object 3D Reconstruction

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    GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection

    Abhinav Kumar, Garrick Brazil, Xiaoming Liu

    Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. While there were attempts to include NMS in the training pipeline for tasks such as 2D object detection, they have been less widely ...

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    Keywords: 3D Object Detection

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    Kinematic 3D Object Detection in Monocular Video

    Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, Bernt Schiele

    Perceiving the physical world in 3D is fundamental for selfdriving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not been thoroughly utilized in modern 3D object detectors. In this work, we propose a novel method for monocular video-based ...

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    Keywords: 3D Object Detection, Video

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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

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    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

2021

  • Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image
    Feng Liu, Xiaoming Liu
    In Proceeding of Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, Dec. 2021
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
  • @inproceedings{ voxel-based-3d-detection-and-reconstruction-of-multiple-objects-from-a-single-image,
      author = { Feng Liu and Xiaoming Liu },
      title = { Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image },
      booktitle = { In Proceeding of Thirty-fifth Conference on Neural Information Processing Systems },
      address = { Virtual },
      month = { December },
      year = { 2021 },
    }
  • GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
    Abhinav Kumar, Garrick Brazil, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, Jun. 2021
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
  • @inproceedings{ groomed-nms-grouped-mathematically-differentiable-nms-for-monocular-3d-object-detection,
      author = { Abhinav Kumar and Garrick Brazil and Xiaoming Liu },
      title = { GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Nashville, TN },
      month = { June },
      year = { 2021 },
    }

2020

  • Kinematic 3D Object Detection in Monocular Video
    Garrick Brazil, Gerard Pons-Moll, Xiaoming Liu, Bernt Schiele
    In Proceeding of European Conference on Computer Vision (ECCV 2020), Virtual, Aug. 2020
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
  • @inproceedings{ kinematic-3d-object-detection-in-monocular-video,
      author = { Garrick Brazil and Gerard Pons-Moll and Xiaoming Liu and Bernt Schiele },
      title = { Kinematic 3D Object Detection in Monocular Video },
      booktitle = { In Proceeding of European Conference on Computer Vision },
      address = { Virtual },
      month = { August },
      year = { 2020 },
    }

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 | Code | 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 },
    }