  Event cameras are bio-inspired sensors that offer several advantages, such as
low latency, high-speed and high dynamic range, to tackle challenging scenarios
in computer vision. This paper presents a solution to the problem of 3D
reconstruction from data captured by a stereo event-camera rig moving in a
static scene, such as in the context of stereo Simultaneous Localization and
Mapping. The proposed method consists of the optimization of an energy function
designed to exploit small-baseline spatio-temporal consistency of events
triggered across both stereo image planes. To improve the density of the
reconstruction and to reduce the uncertainty of the estimation, a probabilistic
depth-fusion strategy is also developed. The resulting method has no special
requirements on either the motion of the stereo event-camera rig or on prior
knowledge about the scene. Experiments demonstrate our method can deal with
both texture-rich scenes as well as sparse scenes, outperforming
state-of-the-art stereo methods based on event data image representations.
