  We propose an active 3D mapping method for depth sensors, which allow
individual control of depth-measuring rays, such as the newly emerging
solid-state lidars. The method simultaneously (i) learns to reconstruct a dense
3D occupancy map from sparse depth measurements, and (ii) optimizes the
reactive control of depth-measuring rays. To make the first step towards the
online control optimization, we propose a fast prioritized greedy algorithm,
which needs to update its cost function in only a small fraction of pos- sible
rays. The approximation ratio of the greedy algorithm is derived. An
experimental evaluation on the subset of the KITTI dataset demonstrates
significant improve- ment in the 3D map accuracy when learning-to-reconstruct
from sparse measurements is coupled with the optimization of depth-measuring
rays.
