3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection

@article{Chen20183DOP,
  title={3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection},
  author={Xiaozhi Chen and Kaustav Kundu and Yukun Zhu and Huimin Ma and Sanja Fidler and Raquel Urtasun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2018},
  volume={40},
  pages={1259-1272}
}
The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals… CONTINUE READING
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