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3D Object Detection

  1. summary image

    SeaBird: Segmentation in Bird’s View with Dice Loss Improves Monocular 3D Detection of Large Objects

    Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren and Xiaoming Liu

    Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute the failures to training data scarcity or receptive field requirements of large objects. In this paper, we highlight this understudied problem of generalization to large objects ...

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    Keywords: 3D Object Detection, Image Segmentation

  2. summary image

    DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection

    Abhinav Kumar, Garrick Brazil, Enrique Corona, Armin Parchami, Xiaoming Liu

    Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for which the ...

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    Keywords: 3D Object Detection

  3. summary image

    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

  4. summary image

    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

  5. summary image

    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

  6. summary image

    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

2024

  • SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
    Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2024), Seattle, WA, Jun. 2024
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code
  • @inproceedings{ seabird-segmentation-in-birds-view-with-dice-loss-improves-monocular-3d-detection-of-large-objects,
      author = { Abhinav Kumar and Yuliang Guo and Xinyu Huang and Liu Ren and Xiaoming Liu },
      title = { SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Seattle, WA },
      month = { June },
      year = { 2024 },
    }

2023

  • RADIANT: RADar Image Association NeTwork for 3D Object Detection
    Yunfei Long, Abhinav Kumar, Daniel Morris, Xiaoming Liu, Marcos Paul Gerardo Castro, Punarjay Chakravarty
    In Proceeding of Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Washington, D.C., Feb. 2023
    Bibtex | PDF
  • @inproceedings{ radiant-radar-image-association-network-for-3d-object-detection,
      author = { Yunfei Long and Abhinav Kumar and Daniel Morris and Xiaoming Liu and Marcos Paul Gerardo Castro and Punarjay Chakravarty },
      title = { RADIANT: RADar Image Association NeTwork for 3D Object Detection },
      booktitle = { In Proceeding of Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI) },
      address = { Washington, D.C. },
      month = { February },
      year = { 2023 },
    }

2022

  • DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
    Abhinav Kumar, Garrick Brazil, Enrique Corona, Armin Parchami, Xiaoming Liu
    In Proceeding of European Conference on Computer Vision (ECCV 2022), Tel-Aviv, Israel, Oct. 2022
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code
  • @inproceedings{ deviant-depth-equivariant-network-for-monocular-3d-object-detection,
      author = { Abhinav Kumar and Garrick Brazil and Enrique Corona and Armin Parchami and Xiaoming Liu },
      title = { DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection },
      booktitle = { In Proceeding of European Conference on Computer Vision },
      address = { Tel-Aviv, Israel },
      month = { October },
      year = { 2022 },
    }

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 },
    }