  In this paper, we tackle the problem of online road network extraction from
sparse 3D point clouds. Our method is inspired by how an annotator builds a
lane graph, by first identifying how many lanes there are and then drawing each
one in turn. We develop a hierarchical recurrent network that attends to
initial regions of a lane boundary and traces them out completely by outputting
a structured polyline. We also propose a novel differentiable loss function
that measures the deviation of the edges of the ground truth polylines and
their predictions. This is more suitable than distances on vertices, as there
exists many ways to draw equivalent polylines. We demonstrate the effectiveness
of our method on a 90 km stretch of highway, and show that we can recover the
right topology 92\% of the time.
