SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving

@article{Yang2020SurfelGANSR,
  title={SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving},
  author={Zhenpei Yang and Yuning Chai and Dragomir Anguelov and Y. Zhou and Pei Sun and D. Erhan and Sean Rafferty and Henrik Kretzschmar},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={11115-11124}
}
Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is hugely helpful. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects, and material properties. Such approaches have limited scalability and fail to produce realistic… Expand
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