In this paper, we present a new approach to bridge the domain gap between
synthetic and real-world data for un- manned aerial vehicle (UAV)-based
perception. Our formu- lation is designed for dynamic scenes, consisting of
moving objects or human actions, where the goal is to recognize the pose or
actions. We propose an extension of K-Planes Neural Radiance Field (NeRF),
wherein our algorithm stores a set of tiered feature vectors. The tiered
feature vectors are generated to effectively model conceptual information about
a scene as well as an image decoder that transforms output feature maps into
RGB images. Our technique leverages the information amongst both static and
dynamic objects within a scene and is able to capture salient scene attributes
of high altitude videos. We evaluate its performance on challenging datasets,
including Okutama Action and UG2, and observe considerable improvement in
accuracy over state of the art aerial perception algorithms.

本论文提出了一种新的方法来缩小合成数据和实际世界数据之间的领域差距，用于无人机感知。我们的方法适用于包含移动物体或人类动作的动态场景，旨在识别姿势或行为。我们扩展了 K-Planes 神经辐射场（NeRF），通过存储一组分层特征向量来有效建模场景的概念信息，并使用图像解码器将输出特征图转换为 RGB 图像。我们的技术利用场景中静态和动态对象之间的信息，并能够捕捉高空视频的显著场景属性。我们在包括 Okutama Action 和 UG2 在内的具有挑战性的数据集上评估其性能，并观察到与现有空中感知算法相比的显著准确性改进。