Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving monocular camera setups, top-down perspective, and multiple small, moving humans, which are not adequately represented in existing datasets. In this work, we introduce UAV4D, a framework for enabling photorealistic rendering for dynamic real-world scenes captured by UAVs. Specifically, we address the challenge of reconstructing dynamic scenes with multiple moving pedestrians from monocular video data without the need for additional sensors. We use a combination of a 3D foundation model and a human mesh reconstruction model to reconstruct both the scene background and humans. We propose a novel approach to resolve the scene scale ambiguity and place both humans and the scene in world coordinates by identifying human-scene contact points. Additionally, we exploit the SMPL model and background mesh to initialize Gaussian splats, enabling holistic scene rendering. We evaluated our method on three complex UAV-captured datasets: VisDrone, Manipal-UAV, and Okutama-Action, each with distinct characteristics and 10-50 humans. Our results demonstrate the benefits of our approach over existing methods in novel view synthesis, achieving a 1.5 dB PSNR improvement and superior visual sharpness.

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.

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 novel-view 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.

The prospects for and the state of the art of adaptable RF hardware are reviewed, focusing primarily on the traditional frequency planning bottleneck, the filtering stages. First, a case is made that even banded systems can be greatly impacted by a modest amount of tuning. This is done by showing the results of a traditional fixed system in an unlicensed band upgraded with a programmable front-end filter. Next, a system built specifically for wideband tuning is shown that enables band selection across the 20-MHz-6-GHz-band. Cooperative operation of multiple colocated nodes is enabled by high-quality pre-LNA filtering across the bands of operation. Future capabilities of adaptable systems are shown by reviewing the state of the art of adaptable systems, heading toward a field-programmable filter array in which a sea of resonators are dynamically interconnected to create a transfer function on demand. Additionally, a novel synthesis approach is highlighted in which multiple filters can cooperate gracefully without crossover issues between the bands. This approach allows for a vast number of filter states by turning on and off passbands without affecting the adjacent bands. The advancements in adaptable hardware will enable new classes of RF systems which much more efficiently utilize the spectrum.