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Semi-Dense 3D Reconstruction with a Stereo Event Camera (ECCV'18)

UZH Robotics and Perception Group
18.2K subscribers
7.4K views 6 years ago
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 spars …...more
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Semi-Dense 3D Reconstruction with a Stereo Event Camera (ECCV'18)
65Likes
7,493Views
2018Aug 17
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. Reference: Yi Zhou, Guillermo Gallego, Henri Rebecq, Laurent Kneip, Hongdong Li, Davide Scaramuzza. Semi-Dense 3D Reconstruction with a Stereo Event Camera. European Conference on Computer Vision (ECCV), Munich, Sept. 2018. PDF: http://rpg.ifi.uzh.ch/docs/ECCV18_Zho... Our research page on event based vision: http://rpg.ifi.uzh.ch/research_dvs.html For event-camera datasets and event camera simulator, see here: http://rpg.ifi.uzh.ch/davis_data.html Other resources on event cameras (publications, software, drivers, where to buy, etc.): https://github.com/uzh-rpg/event-base... Affiliations: Y. Zhou and H. Li are with the Australian National University and the Australian Centre for Robotics, Canberra, Australia. https://www.roboticvision.org/ G. Gallego, H. Rebecq and D. Scaramuzza are with the Robotics and Perception Group, Dept. of Informatics, University of Zurich, and Dept. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland http://rpg.ifi.uzh.ch/ L. Kneip is with the School of Informaiton Science and Technology, ShanghaiTech University, China. http://sist.shanghaitech.edu.cn/

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UZH Robotics and Perception Group

18.2K subscribers