TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes

In this paper, we present a new approach to improve the neural rendering fidelity of in-the-wild unmanned aerial vehicle (UAV)-based scenes. Our formulation is designed for dynamic scenes, consisting of small moving objects or human actions in particular. We propose an extension of K-Planes Neural Radiance Field (NeRF), wherein our algorithm stores a set of tiered high dimensional feature vectors. The tiered feature vectors are generated to effectively model conceptual information about a scene as well as to be processed by an image decoder that transforms output feature maps into RGB images. Our technique leverages the information among 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 neural rendering methods.