  We estimate the radiance field of large-scale dynamic areas from multiple
vehicle captures under varying environmental conditions. Previous works in this
domain are either restricted to static environments, do not scale to more than
a single short video, or struggle to separately represent dynamic object
instances. To this end, we present a novel, decomposable radiance field
approach for dynamic urban environments. We propose a multi-level neural scene
graph representation that scales to thousands of images from dozens of
sequences with hundreds of fast-moving objects. To enable efficient training
and rendering of our representation, we develop a fast composite ray sampling
and rendering scheme. To test our approach in urban driving scenarios, we
introduce a new, novel view synthesis benchmark. We show that our approach
outperforms prior art by a significant margin on both established and our
proposed benchmark while being faster in training and rendering.
