Depth from time-of-flight using machine learning S Nowozin, A Adam, S Mazor, O Yair US Patent 9,760,837, 2017 | 85 | 2017 |
Bayesian time-of-flight for realtime shape, illumination and albedo A Adam, C Dann, O Yair, S Mazor, S Nowozin IEEE transactions on pattern analysis and machine intelligence 39 (5), 851-864, 2016 | 43 | 2016 |
Temporal time-of-flight A Adam, S Nowozin, O Yair, S Mazor, M Schober US Patent 10,229,502, 2019 | 42 | 2019 |
Dynamic time-of-flight M Schober, A Adam, O Yair, S Mazor, S Nowozin Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 15 | 2017 |
Contrastive Divergence Learning is a Time Reversal Adversarial Game O Yair, T Michaeli arXiv preprint arXiv:2012.03295, 2020 | 9 | 2020 |
Uncertainty quantification via neural posterior principal components E Nehme, O Yair, T Michaeli Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
Depth from time-of-flight using machine learning S Nowozin, A Adam, S Mazor, O Yair US Patent 10,311,378, 2019 | 5 | 2019 |
Thinking fourth dimensionally: Treating Time as a Random Variable in EBMs O Yair, T Michaeli | 2 | 2022 |
Uncertainty Visualization via Low-Dimensional Posterior Projections O Yair, E Nehme, T Michaeli arXiv preprint arXiv:2312.07804, 2023 | | 2023 |