  Photorealistic simulation plays a crucial role in applications such as
autonomous driving, where advances in neural radiance fields (NeRFs) may allow
better scalability through the automatic creation of digital 3D assets.
However, reconstruction quality suffers on street scenes due to largely
collinear camera motions and sparser samplings at higher speeds. On the other
hand, the application often demands rendering from camera views that deviate
from the inputs to accurately simulate behaviors like lane changes. In this
paper, we propose several insights that allow a better utilization of Lidar
data to improve NeRF quality on street scenes. First, our framework learns a
geometric scene representation from Lidar, which is fused with the implicit
grid-based representation for radiance decoding, thereby supplying stronger
geometric information offered by explicit point cloud. Second, we put forth a
robust occlusion-aware depth supervision scheme, which allows utilizing
densified Lidar points by accumulation. Third, we generate augmented training
views from Lidar points for further improvement. Our insights translate to
largely improved novel view synthesis under real driving scenes.
