Learning Depth-Guided Convolutions for Monocular 3D Object Detection

@article{Ding2020LearningDC,
  title={Learning Depth-Guided Convolutions for Monocular 3D Object Detection},
  author={Mingyu Ding and Yuqi Huo and Hongwei Yi and Zhe Wang and Jianping Shi and Zhiwu Lu and Ping Luo},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2020},
  pages={4306-4315}
}
  • Mingyu Ding, Yuqi Huo, P. Luo
  • Published 10 December 2019
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo-LiDAR representation, and then apply existing 3D point-cloud based object detectors. However… 
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