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.

翻译：无人机成像条件存在巨大变化且自由度较高，导致基于无人机的感知模型在充分学习时严重缺乏数据。在地面成像领域，将各类合成渲染器与感知模型结合使用来生成合成数据以增强学习效果已较为普遍。然而，严苛的无人机领域仍面临严峻挑战，需要采用独特的图像合成方案来解决数据增强问题。本文利用神经渲染领域的最新进展，改进静态与动态新视角的无人机图像合成技术——尤其针对高空环境下的关键场景特征捕捉。最后我们证明，当最先进的检测模型主要基于真实与合成数据的混合集进行优化时，其性能提升效果显著优于单独使用真实数据或合成数据。