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Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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21#
發(fā)表于 2025-3-25 04:07:30 | 只看該作者
https://doi.org/10.1007/978-981-99-6335-5nder different light conditions. This can be later used to reveal surface details and interactively relight the subject. Such process, however, typically requires dedicated hardware setups to recover the light direction from multiple locations, making the process tedious when performed outside the l
22#
發(fā)表于 2025-3-25 07:40:49 | 只看該作者
23#
發(fā)表于 2025-3-25 15:28:35 | 只看該作者
The Effects of Noise on Aquatic Life our method. Owing to global optimisation, our algorithm is much more capable at recovering the true divergence, compared to other heuristic divergence estimators or event-based optic flow methods. With GPU acceleration, our method also achieves competitive runtimes.
24#
發(fā)表于 2025-3-25 18:36:03 | 只看該作者
Arthur N. Popper,Anthony D. Hawkinsdata augmentation. By preparing models at the training stage for the appropriate feature space, the need for additional computational resources on-board for e.g. image scaling or band-alignment of camera data can be mitigated.
25#
發(fā)表于 2025-3-25 21:11:23 | 只看該作者
26#
發(fā)表于 2025-3-26 00:16:17 | 只看該作者
27#
發(fā)表于 2025-3-26 04:33:51 | 只看該作者
28#
發(fā)表于 2025-3-26 10:48:53 | 只看該作者
29#
發(fā)表于 2025-3-26 15:57:10 | 只看該作者
Spacecraft Pose Estimation Based on?Unsupervised Domain Adaptation and?on a?3D-Guided Loss Combinatiomain adaptation with robust pseudo-labelling. Our solution has ranked second in the two categories of the 2021 Pose Estimation Challenge organised by the European Space Agency and the Stanford University, achieving the lowest average error over the two categories (Code is available at: .).
30#
發(fā)表于 2025-3-26 18:20:12 | 只看該作者
Monocular 6-DoF Pose Estimation for?Non-cooperative Spacecrafts Using Riemannian Regression Networkon and limits the error to a desirable range. Moreover, several data augmentation techniques were proposed to address the overfitting issue caused by the small scale of the spacecraft dataset in this paper. In the SPARK2022 challenge, our method achieves state-of-the-art pose estimation accuracy.
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