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Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing. (arXiv:2209.11945v1 [cs.CV])
Sept. 27, 2022, 1:12 a.m. | Mohsi Jawaid, Ethan Elms, Yasir Latif, Tat-Jun Chin
cs.CV updates on arXiv.org arxiv.org
Deep models trained using synthetic data require domain adaptation to bridge
the gap between the simulation and target environments. State-of-the-art domain
adaptation methods often demand sufficient amounts of (unlabelled) data from
the target domain. However, this need is difficult to fulfil when the target
domain is an extreme environment, such as space. In this paper, our target
problem is close proximity satellite pose estimation, where it is costly to
obtain images of satellites from actual rendezvous missions. We demonstrate
that …
More from arxiv.org / cs.CV updates on arXiv.org
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