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This paper is about the efficient generation of dense, colored models ofcity-scale environments from range data and in particular, stereo cameras.Better maps make for better understanding; better understanding leads to betterrobots, but this comes at a cost. The computational and memory requirements oflarge dense models can be prohibitive. We provide the theory and the systemneeded to create city-scale dense reconstructions. To do so, we apply aregularizer over a compressed 3D data structure while dealing with the complexboundary conditions this induces during the data-fusion stage. We show thatonly with these considerations can we swiftly create neat, large, "wellbehaved" reconstructions. We evaluate our system using the KITTI dataset andprovide statistics for the metric errors in all surfaces created compared tothose 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. Forsubjective analysis, we provide a qualitative review of 6.1 km of our densereconstructions in an attached video. These are the largest densereconstructions from a single passive camera we are aware of in the literature.

DENSER Cities: A System for Dense Efficient Reconstructions of Cities