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

• URL: http://arxiv.org/abs/2310.16255v1
• Date: Wed, 25 Oct 2023 00:20:37 GMT
• Title: UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception
• Authors: Christopher Maxey, Jaehoon Choi, Hyungtae Lee, Dinesh Manocha, Heesung Kwon
• Abstract summary: We leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image rendering. We demonstrate a considerable performance boost when a state-of-the-art detection model is optimized primarily on hybrid sets of real and synthetic data.
• Score: 62.71374902455154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-ofthe-art detection model is optimized primarily on hybrid sets of real and synthetic data instead of the real or synthetic data separately.

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