Neural radiance fields~(NeRF) have recently been applied to render
large-scale scenes. However, their limited model capacity typically results in
blurred rendering results. Existing large-scale NeRFs primarily address this
limitation by partitioning the scene into blocks, which are subsequently
handled by separate sub-NeRFs. These sub-NeRFs, trained from scratch and
processed independently, lead to inconsistencies in geometry and appearance
across the scene. Consequently, the rendering quality fails to exhibit
significant improvement despite the expansion of model capacity. In this work,
we present global-guided focal neural radiance field (GF-NeRF) that achieves
high-fidelity rendering of large-scale scenes. Our proposed GF-NeRF utilizes a
two-stage (Global and Focal) architecture and a global-guided training
strategy. The global stage obtains a continuous representation of the entire
scene while the focal stage decomposes the scene into multiple blocks and
further processes them with distinct sub-encoders. Leveraging this two-stage
architecture, sub-encoders only need fine-tuning based on the global encoder,
thus reducing training complexity in the focal stage while maintaining
scene-wide consistency. Spatial information and error information from the
global stage also benefit the sub-encoders to focus on crucial areas and
effectively capture more details of large-scale scenes. Notably, our approach
does not rely on any prior knowledge about the target scene, attributing
GF-NeRF adaptable to various large-scale scene types, including street-view and
aerial-view scenes. We demonstrate that our method achieves high-fidelity,
natural rendering results on various types of large-scale datasets. Our project
page: this https URL

提出了一种全球引导的焦点神经辐射场 (GF-NeRF)，利用两阶段结构和全球引导的训练策略，在大规模场景中实现高保真度的渲染结果。

全球引导的焦点神经辐射场用于大规模场景渲染

Global-guided Focal Neural Radiance Field for Large-scale Scene  Rendering

Purely MLP-based neural radiance fields (NeRF-based methods) often suffer
from underfitting with blurred renderings on large-scale scenes due to limited
model capacity. Recent approaches propose to geographically divide the scene
and adopt multiple sub-NeRFs to model each region individually, leading to
linear scale-up in training costs and the number of sub-NeRFs as the scene
expands. An alternative solution is to use a feature grid representation, which
is computationally efficient and can naturally scale to a large scene with
increased grid resolutions. However, the feature grid tends to be less
constrained and often reaches suboptimal solutions, producing noisy artifacts
in renderings, especially in regions with complex geometry and texture. In this
work, we present a new framework that realizes high-fidelity rendering on large
urban scenes while being computationally efficient. We propose to use a compact
multiresolution ground feature plane representation to coarsely capture the
scene, and complement it with positional encoding inputs through another NeRF
branch for rendering in a joint learning fashion. We show that such an
integration can utilize the advantages of two alternative solutions: a
light-weighted NeRF is sufficient, under the guidance of the feature grid
representation, to render photorealistic novel views with fine details; and the
jointly optimized ground feature planes, can meanwhile gain further
refinements, forming a more accurate and compact feature space and output much
more natural rendering results.

该研究提出了一个新的框架用于实现对大型城市场景的高保真渲染，该框架运用了一种简化的多分辨率地面特征平面表示来捕捉场景，并通过另一个 NeRF 分支的位置编码输入进行补充，以联合学习的方式进行渲染。