  This paper is about the efficient generation of dense, colored models of
city-scale environments from range data and in particular, stereo cameras.
Better maps make for better understanding; better understanding leads to better
robots, but this comes at a cost. The computational and memory requirements of
large dense models can be prohibitive. We provide the theory and the system
needed to create city-scale dense reconstructions. To do so, we apply a
regularizer over a compressed 3D data structure while dealing with the complex
boundary conditions this induces during the data-fusion stage. We show that
only with these considerations can we swiftly create neat, large, "well
behaved" reconstructions. We evaluate our system using the KITTI dataset and
provide statistics for the metric errors in all surfaces created compared to
those measured with 3D laser. Our regularizer reduces the median error by 40%
in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For
subjective analysis, we provide a qualitative review of 6.1 km of our dense
reconstructions in an attached video. These are the largest dense
reconstructions from a single passive camera we are aware of in the literature.
