Despite the recent success of Neural Radiance Field (NeRF), it is still
challenging to render large-scale driving scenes with long trajectories,
particularly when the rendering quality and efficiency are in high demand.
Existing methods for such scenes usually involve with spatial warping,
geometric supervision from zero-shot normal or depth estimation, or scene
division strategies, where the synthesized views are often blurry or fail to
meet the requirement of efficient rendering. To address the above challenges,
this paper presents a novel framework that learns a density space from the
scenes to guide the construction of a point-based renderer, dubbed as DGNR
(Density-Guided Neural Rendering). In DGNR, geometric priors are no longer
needed, which can be intrinsically learned from the density space through
volumetric rendering. Specifically, we make use of a differentiable renderer to
synthesize images from the neural density features obtained from the learned
density space. A density-based fusion module and geometric regularization are
proposed to optimize the density space. By conducting experiments on a widely
used autonomous driving dataset, we have validated the effectiveness of DGNR in
synthesizing photorealistic driving scenes and achieving real-time capable
rendering.

通过学习密度空间并引导构建点云渲染器的新框架 DGNR（Density-Guided Neural Rendering）解决了 Neural Radiance Field 在渲染大规模驾驶场景时的挑战，实现了合适的渲染质量和高效率。