Semi-dense 3D Reconstruction with a Stereo Event Camera

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.The research leading to these results is supported by the Australian Centre for Robotic Vision and the National Center of Competence in Research (NCCR) Robotics, through the Swiss National Science Foundation, the SNSF-ERC Starting Grant and the NCCR Ph.D. Exchange Scholarship Programme. Yi Zhou also acknowledges the financial support from the China Scholarship Council for his Ph.D. Scholarship No. 201406020098