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Contribution Details
| Type | Book Chapter |
| Scope | Discipline-based scholarship |
| Title | Semi-dense 3D Reconstruction with a Stereo Event Camera |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
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| Booktitle | Computer Vision – ECCV 2018 |
| ISBN | 978-3-030-01245-8 |
| Number | 11205 |
| Place of Publication | Cham |
| Publisher | Springer |
| Page Range | 242 - 258 |
| Date | 2018 |
| Abstract Text | Event cameras are bio-inspired sensors that oer 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. |
| Digital Object Identifier | 10.1007/978-3-030-01246-5_15 |
| Other Identification Number | merlin-id:18691 |
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| Additional Information | 978-3-030-01246-5 (E) |