1. 

   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

2. 

   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

3. 

   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

4. 

   The Edge of Depth: Explicit Constraints between Segmentation and Depth

   Shengjie Zhu, Garrick Brazil, Xiaoming Liu

   In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsupervised monocular depth estimation, constraints from semantic segmentation has been explored implicitly such as sharing and transforming features. In contrast, we propose to explicitly ...

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   Keywords: Depth Prediction, Image Segmentation, Semantic Segmentation

5. 

   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

6. 

   Pedestrian Detection with Autoregressive Network Phases

   Garrick Brazil, Xiaoming Liu

   We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision. The proposed framework utilizes a novel lightweight stackable decoder-encoder module which uses convolutional re-sampling layers to improve features while maintaining efficient memory and runtime cost. Unlike previous cascaded detection systems, our proposed framework is designed ...

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   Keywords: Pedestrian Detection, Object Detection

7. 

   Recurrent Flow-Guided Semantic Forecasting

   Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu

   Understanding the world around us and making decisions about the future is a critical component to human intelligence. As autonomous systems continue to develop, their ability to reason about the future will be the key to their success. Semantic anticipation is a relatively under-explored area for which autonomous vehicles could ...

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   Keywords: Forecasting, Semantic Segmentation, Image Segmentation

8. 

   Illuminating Pedestrians via Simultaneous Detection & Segmentation

   Garrick Brazil, Xi Yin, Xiaoming Liu

   Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to ...

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   Keywords: Pedestrian Detection, Semantic Segmentation, Object Detection, Image Segmentation

2023

• Camera Self-Calibration Using Human Faces
  Masa Hu, Garrick Brazil, Nanxiang Li, Liu Ren, Xiaoming Liu
  In Proceedings of the 17th International Conference on Automatic Face and Gesture Recognition (FG), Hawaii, USA, Jan. 2023
  Bibtex | PDF
@inproceedings{ camera-self-calibration-using-human-faces,
  author = { Masa Hu and Garrick Brazil and Nanxiang Li and Liu Ren and Xiaoming Liu },
  title = { Camera Self-Calibration Using Human Faces },
  booktitle = { In Proceedings of the 17th International Conference on Automatic Face and Gesture Recognition (FG) },
  address = { Hawaii, USA },
  month = { January },
  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

• 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 },
}
• The Edge of Depth: Explicit Constraints between Segmentation and Depth
  Shengjie Zhu, Garrick Brazil, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, Jun. 2020
  Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code | Video
@inproceedings{ the-edge-of-depth-explicit-constraints-between-segmentation-and-depth,
  author = { Shengjie Zhu and Garrick Brazil and Xiaoming Liu },
  title = { The Edge of Depth: Explicit Constraints between Segmentation and Depth },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Seattle, WA },
  month = { June },
  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 },
}
• Pedestrian Detection with Autoregressive Network Phases
  Garrick Brazil, Xiaoming Liu
  In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019
  Bibtex | PDF | arXiv | Poster | Project Webpage | Code
@inproceedings{ pedestrian-detection-with-autoregressive-network-phases,
  author = { Garrick Brazil and Xiaoming Liu },
  title = { Pedestrian Detection with Autoregressive Network Phases },
  booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
  address = { Long Beach, CA },
  month = { June },
  year = { 2019 },
}
• Recurrent Flow-Guided Semantic Forecasting
  Adam M. Terwilliger, Garrick Brazil, Xiaoming Liu
  Proc. IEEE Winter Conference on Application of Computer Vision (WACV 2019), Hawaii, Jan. 2019
  Bibtex | PDF | arXiv | Poster | Project Webpage | Code
@inproceedings{ recurrent-flow-guided-semantic-forecasting,
  author = { Adam M. Terwilliger and Garrick Brazil and Xiaoming Liu },
  title = { Recurrent Flow-Guided Semantic Forecasting },
  booktitle = { Proc. IEEE Winter Conference on Application of Computer Vision },
  address = { Hawaii },
  month = { January },
  year = { 2019 },
}

2017

• Illuminating Pedestrians via Simultaneous Detection & Segmentation
  Garrick Brazil, Xi Yin, Xiaoming Liu
  In Proceeding of International Conference on Computer Vision (ICCV 2017), Venice, Italy, Oct. 2017
  Bibtex | PDF | arXiv | Poster | Project Webpage | Code
@inproceedings{ illuminating-pedestrians-via-simultaneous-detection-segmentation,
  author = { Garrick Brazil and Xi Yin and Xiaoming Liu },
  title = { Illuminating Pedestrians via Simultaneous Detection & Segmentation },
  booktitle = { In Proceeding of International Conference on Computer Vision },
  address = { Venice, Italy },
  month = { October },
  year = { 2017 },
}