  Existing neural radiance fields (NeRF)-based novel view synthesis methods for
large-scale outdoor scenes are mainly built on a single altitude. Moreover,
they often require a priori camera shooting height and scene scope, leading to
inefficient and impractical applications when camera altitude changes. In this
work, we propose an end-to-end framework, termed AG-NeRF, and seek to reduce
the training cost of building good reconstructions by synthesizing
free-viewpoint images based on varying altitudes of scenes. Specifically, to
tackle the detail variation problem from low altitude (drone-level) to high
altitude (satellite-level), a source image selection method and an
attention-based feature fusion approach are developed to extract and fuse the
most relevant features of target view from multi-height images for
high-fidelity rendering. Extensive experiments demonstrate that AG-NeRF
achieves SOTA performance on 56 Leonard and Transamerica benchmarks and only
requires a half hour of training time to reach the competitive PSNR as compared
to the latest BungeeNeRF.
