  Urban reconstruction from a video captured by a surveying vehicle constitutes
a core module of automated mapping. When computational power represents a
limited resource and, a detailed map is not the primary goal, the
reconstruction can be performed incrementally, from a monocular video, carving
a 3D Delaunay triangulation of sparse points; this allows online incremental
mapping for tasks such as traversability analysis or obstacle avoidance. To
exploit the sharp edges of urban landscape, we propose to use a Delaunay
triangulation of Edge-Points, which are the 3D points corresponding to image
edges. These points constrain the edges of the 3D Delaunay triangulation to
real-world edges. Besides the use of the Edge-Points, a second contribution of
this paper is the Inverse Cone Heuristic that preemptively avoids the creation
of artifacts in the reconstructed manifold surface. We force the reconstruction
of a manifold surface since it makes it possible to apply computer graphics or
photometric refinement algorithms to the output mesh. We evaluated our approach
on four real sequences of the public available KITTI dataset by comparing the
incremental reconstruction against Velodyne measurements.
