BirdNeRF: fast neural reconstruction of large-scale scenes from aerial imagery

Abstract

In this study, we introduce BirdNeRF, an adaptation of Neural Radiance Fields (NeRF) specifically designed for reconstructing large-scale scenes using aerial imagery. Unlike previous research which focused on small-scale and object-centric NeRF reconstruction, our approach addresses multiple challenges, including (1) Addressing the issue of slow training and rendering associated with large models. (2) Meeting the computational demands necessitated by modeling a substantial number of images, requiring extensive resources such as high-performance GPUs. (3) Overcoming significant artifacts and low visual fidelity commonly observed in large-scale reconstruction tasks due to limited model capacity. Specifically, we present a novel bird-view pose-based spatial decomposition algorithm. This algorithm decomposes a large aerial image set into multiple small sets with appropriately sized overlaps, allowing us to train individual NeRFs of sub-scene. This decomposition approach enables rendering to scale seamlessly to arbitrarily large environments. Moreover, it allows for per-block updates of the environment, enhancing the flexibility and adaptability of the reconstruction process. Additionally, we propose a projection-guided novel view re-rendering strategy, which aids in effectively utilizing the independently trained sub-scenes to generate superior rendering results. We evaluate our approach on existing datasets as well as against our own drone footage, achieving a reconstruction speed improvement of 10x over classical photogrammetry software and 50x over the state-of-the-art large-scale NeRF solution, all on a single GPU with comparable or superior rendering quality.