  In this paper we address the problem of detecting crosswalks from LiDAR and
camera imagery. Towards this goal, given multiple LiDAR sweeps and the
corresponding imagery, we project both inputs onto the ground surface to
produce a top down view of the scene. We then leverage convolutional neural
networks to extract semantic cues about the location of the crosswalks. These
are then used in combination with road centerlines from freely available maps
(e.g., OpenStreetMaps) to solve a structured optimization problem which draws
the final crosswalk boundaries. Our experiments over crosswalks in a large city
area show that 96.6% automation can be achieved.
