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。