3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks

@article{Ganguly20213DFFSF3,
  title={3D-FFS: Faster 3D object detection with Focused Frustum Search in sensor fusion based networks},
  author={Aniruddha Ganguly and Tasin Ishmam and Khandker Aftarul Islam and Md. Zahidur Rahman and Md. Shamsuzzoha Bayzid},
  journal={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2021},
  pages={6848-6853}
}
  • A. Ganguly, Tasin Ishmam, M. Bayzid
  • Published 15 March 2021
  • Computer Science
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
In this work we propose 3D-FFS, a novel approach to make sensor fusion based 3D object detection networks significantly faster using a class of computationally inexpensive heuristics. Existing sensor fusion based networks generate 3D region proposals by leveraging inferences from 2D object detectors. However, as images have no depth information, these networks rely on extracting semantic features of points from the entire scene to locate the object. By leveraging aggregated intrinsic properties… 

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