  We present an efficient neural 3D scene representation for novel-view
synthesis (NVS) in large-scale, dynamic urban areas. Existing works are not
well suited for applications like mixed-reality or closed-loop simulation due
to their limited visual quality and non-interactive rendering speeds. Recently,
rasterization-based approaches have achieved high-quality NVS at impressive
speeds. However, these methods are limited to small-scale, homogeneous data,
i.e. they cannot handle severe appearance and geometry variations due to
weather, season, and lighting and do not scale to larger, dynamic areas with
thousands of images. We propose 4DGF, a neural scene representation that scales
to large-scale dynamic urban areas, handles heterogeneous input data, and
substantially improves rendering speeds. We use 3D Gaussians as an efficient
geometry scaffold while relying on neural fields as a compact and flexible
appearance model. We integrate scene dynamics via a scene graph at global scale
while modeling articulated motions on a local level via deformations. This
decomposed approach enables flexible scene composition suitable for real-world
applications. In experiments, we surpass the state-of-the-art by over 3 dB in
PSNR and more than 200 times in rendering speed.
