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.

翻译：神经辐射场（NeRF）在建模3D物体和受控场景（通常为单一尺度）方面取得了卓越性能。本文聚焦于多尺度场景，其特点是在截然不同的尺度下观测到显著的影像变化。此类场景广泛存在于现实3D环境中，例如城市场景中从卫星层级（俯瞰城市全貌）到地面层级（呈现建筑复杂细节）的视角，也常见于景观和精细的Minecraft 3D模型。这些场景中视角位置的巨大跨度会产生细节层次差异极大的多尺度渲染，给神经辐射场带来巨大挑战，并使其偏向于折中的结果。为解决这些问题，我们提出BungeeNeRF，一种渐进式神经辐射场，可在极端变化的尺度下实现细节层次渲染。该方法从使用浅层基础模块拟合远距离视图开始，随着训练推进，逐步添加新模块以适应越来越近的视图中涌现的细节。该策略渐进式地激活NeRF位置编码输入中的高频通道，并在训练过程中逐步展开更复杂的细节。我们通过多数据源（城市模型、合成数据和无人机采集数据）的实验，证明了BungeeNeRF在建模视角变化极大的多尺度场景中的优越性，以及其对不同细节层次高质量渲染的支持。