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pro vyhledávání: '"Puscas, Mihai Marian"'
Autor:
Pilzer, Andrea, Lathuilière, Stéphane, Xu, Dan, Puscas, Mihai Marian, Ricci, Elisa, Sebe, Nicu
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during training. To cope with this issue, in this paper we present a novel un
Externí odkaz:
http://arxiv.org/abs/1909.07667
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Ran
Externí odkaz:
http://arxiv.org/abs/1908.05794
While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during training. To cope with this issue, in this paper we present a novel unsup
Externí odkaz:
http://arxiv.org/abs/1807.10915
Akademický článek
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Publikováno v:
2015 IEEE International Conference on Computer Vision (ICCV); 1/1/2015, p1653-1661, 9p
Publikováno v:
2015 IEEE International Conference on Computer Vision (ICCV); 1/1/2015, p4714-4729, 16p