Uncertainty Guided Progressive Few-Shot Learning Perception for Aerial View Synthesis

Zihan Gao, Lingling Li, Xu Liu, Licheng Jiao, Fang Liu, Shuyuan Yang

Abstract: View synthesis of aerial scenes has gained attention in the recent development of applications such as urban planning, navigation, and disaster assessment. This development is closely connected to the recent advancement of the Neural Radiance Field (NeRF). However, when autonomousaerial vehicles(AAVs) encounter constraints such as limited perspectives or energy limitations, NeRF degrades with sparsely sampled views in complex aerial scenes. On this basis, we aim to solve this problem in a few-shot manner. In this paper, we propose Uncertainty Guided Perception NeRF (UPNeRF), an uncertainty-guided perceptual learning framework that focuses on applying and improving NeRF in few-shot aerial view synthesis (FSAVS). First, simply optimizing NeRF in complex aerial scenes with sparse input can lead to overfitting in training views, resulting in a collapsed model. To address this, we propose a progressive learning strategy that utilizes the uncertainty present in sparsely sampled views, enabling a gradual transition from easy to hard learning. Second, to take advantage of the inherent inductive bias in the data, we introduce an uncertainty-aware discriminator. This discriminator leverages convolutional capabilities to capture intricate patterns in the rendered patches associated with uncertainty. Third, direct optimization of NeRF lacks prior knowledge of the scene. This, coupled with a reduction in training views, can result in unrealistic rendering. To overcome this, we present a perceptual regularizer that incorporates prior knowledge through prompt tuning of a self-supervised pre-trained vision transformer. In addition, we adopt a sampled scene annealing strategy to enhance training stability. Finally, we conducted experiments with two public datasets, and the positive results indicate our method is effective.