  Neural radiance fields (NeRF) has achieved outstanding performance in
modeling 3D objects and controlled scenes, usually under a single scale. In
this work, we focus on multi-scale cases where large changes in imagery are
observed at drastically different scales. This scenario vastly exists in
real-world 3D environments, such as city scenes, with views ranging from
satellite level that captures the overview of a city, to ground level imagery
showing complex details of an architecture; and can also be commonly identified
in landscape and delicate minecraft 3D models. The wide span of viewing
positions within these scenes yields multi-scale renderings with very different
levels of detail, which poses great challenges to neural radiance field and
biases it towards compromised results. To address these issues, we introduce
BungeeNeRF, a progressive neural radiance field that achieves level-of-detail
rendering across drastically varied scales. Starting from fitting distant views
with a shallow base block, as training progresses, new blocks are appended to
accommodate the emerging details in the increasingly closer views. The strategy
progressively activates high-frequency channels in NeRF's positional encoding
inputs and successively unfolds more complex details as the training proceeds.
We demonstrate the superiority of BungeeNeRF in modeling diverse multi-scale
scenes with drastically varying views on multiple data sources (city models,
synthetic, and drone captured data) and its support for high-quality rendering
in different levels of detail.
