Abstract. 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