Learning to Drop Points for LiDAR Scan Synthesis

  title={Learning to Drop Points for LiDAR Scan Synthesis},
  author={Kazuto Nakashima and Ryo Kurazume},
  journal={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  • Kazuto Nakashima, R. Kurazume
  • Published 23 February 2021
  • Computer Science, Environmental Science
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on. Generative modeling of LiDAR data as scene priors is one of the promising solutions to compensate for unreliable or incomplete observations. In this paper, we propose a novel generative model for learning LiDAR data based on generative adversarial networks. As in… 

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