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arXiv · 1807.07429

Semi-Dense 3D Reconstruction with a Stereo Event Camera

Yi Zhou·Guillermo Gallego·Henri Rebecq·Laurent Kneip·Hongdong Li·Davide Scaramuzza
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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.

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Yi Zhou, Guillermo Gallego, Henri Rebecq, Laurent Kneip, Hongdong Li, Davide Scaramuzza. 2018-07-19. Semi-Dense 3D Reconstruction with a Stereo Event Camera. https://doi.org/10.1007/978-3-030-01246-5_15

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