An Occlusion Signal-Processing Framework Based on UAV Sampling for Improving Rendering Quality of Views

Using unmanned aerial vehicles (UAV) for large-scale scene sampling is a prevalent application in UAV vision. However, there are certain factors that can influence the quality of UAV sampling, such as the lack of texture details and drastic changes in scene geometry. One common factor is occlusion, which is a surface feature in 3D scenes that results in significant discontinuity on the scene surface, leading to transient noise and loss of local information. This can cause degradation in the performance of computer vision algorithms. To address these challenges, this paper proposes a UAV sampling method that takes into account occlusion. The method is based on the principle of quantizing occlusion information and improves the aerial light field (ALF) technology. It establishes a UAV ALF sampling model that considers scene occlusion information and calculates the minimum sampling rate of UAV sampling by deriving the exact expression of the spectrum. The proposed model is used to sample and reconstruct large-scale scenes in different occlusion environments. Experimental results demonstrate that the model effectively improves the reconstruction quality of large-scale scenes in occluded environments.