  The goal of this work is to perform 3D reconstruction and novel view
synthesis from data captured by scanning platforms commonly deployed for world
mapping in urban outdoor environments (e.g., Street View). Given a sequence of
posed RGB images and lidar sweeps acquired by cameras and scanners moving
through an outdoor scene, we produce a model from which 3D surfaces can be
extracted and novel RGB images can be synthesized. Our approach extends Neural
Radiance Fields, which has been demonstrated to synthesize realistic novel
images for small scenes in controlled settings, with new methods for leveraging
asynchronously captured lidar data, for addressing exposure variation between
captured images, and for leveraging predicted image segmentations to supervise
densities on rays pointing at the sky. Each of these three extensions provides
significant performance improvements in experiments on Street View data. Our
system produces state-of-the-art 3D surface reconstructions and synthesizes
higher quality novel views in comparison to both traditional methods
(e.g.~COLMAP) and recent neural representations (e.g.~Mip-NeRF).
