UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception

UAVSim: Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models is prevalent to create synthetic data to augment the learning in the ground-based imaging domain. However, severe challenges in the austere UAV-based domain require distinctive solutions to image synthesis for data augmentation. In this work, we leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image synthesis, especially from high altitudes, capturing salient scene attributes. Finally, we demonstrate a considerable performance boost is achieved when a state-of-the-art detection model is optimized primarily on hybrid sets of real and synthetic data instead of the real or synthetic data separately.

@article{Chris2023UAVSim,
  title={UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception},
  author={Christopher Maxey, Jaehoon Choi, Hyungtae Lee, Dinesh Manocha, Heesung Kwon},
  journal={ArXiv},
  year={2023},
  volume={abs/2310.16255}
}