Abstract. 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