PLUMENet: Efficient 3D Object Detection from Stereo Images

@article{Wang2021PLUMENetE3,
  title={PLUMENet: Efficient 3D Object Detection from Stereo Images},
  author={Yan Wang and Binh Yang and Rui Hu and Ming Liang and Raquel Urtasun},
  journal={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2021},
  pages={3383-3390}
}
  • Yan Wang, Binh Yang, R. Urtasun
  • Published 17 January 2021
  • Computer Science
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to produce a pseudo LiDAR point cloud, which is then used as input to a 3D object detector. However… 
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