Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Mariotti, Octave"'
Autor:
Esposito, Salvatore, Xu, Qingshan, Kania, Kacper, Hewitt, Charlie, Mariotti, Octave, Petikam, Lohit, Valentin, Julien, Onken, Arno, Mac Aodha, Oisin
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. Most existing approaches predict volumetric density to render multi-view consistent images. By employing volumetric rendering using neural ra
Externí odkaz:
http://arxiv.org/abs/2406.04254
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19521-19530
Recent progress in self-supervised representation learning has resulted in models that are capable of extracting image features that are not only effective at encoding image level, but also pixel-level, semantics. These features have been shown to be
Externí odkaz:
http://arxiv.org/abs/2312.13216
We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain. We apply this representation to reconstruct explicit surfaces from multiple view
Externí odkaz:
http://arxiv.org/abs/2306.02956
Publikováno v:
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10418-10428
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the p
Externí odkaz:
http://arxiv.org/abs/2212.00435
Publikováno v:
Proceedings of the 33rd British Machine Vision Conference, BMVC 2022
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions
Externí odkaz:
http://arxiv.org/abs/2212.00436
Autor:
Mariotti, Octave, Bilen, Hakan
Publikováno v:
ECCV 2020: Computer Vision - ECCV 2020 Workshops pp 631-647
There is a growing interest in developing computer vision methods that can learn from limited supervision. In this paper, we consider the problem of learning to predict camera viewpoints, where obtaining ground-truth annotations are expensive and req
Externí odkaz:
http://arxiv.org/abs/2104.01103
Autor:
Mariotti, Octave
The recent progress in deep learning techniques transformed the field of computer vision, with tasks like object classification or segmentation being almost considered solved. This however requires sufficiently many labeled samples to train the syste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______463::4176e2fe7dc39c9a907ec6220c2ae13a
https://hdl.handle.net/1842/40529
https://hdl.handle.net/1842/40529
Publikováno v:
Mariotti, O, Mac Aodha, O & Bilen, H 2022, ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation . in Proceedings of 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021 . 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10398-10408, International Conference on Computer Vision 2021, 11/10/21 . https://doi.org/10.1109/ICCV48922.2021.01025
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::876189b63fd0d0fef2fc37af42d0f84f
https://doi.org/10.1109/iccv48922.2021.01025
https://doi.org/10.1109/iccv48922.2021.01025
Publikováno v:
Computer Graphics Forum. Oct2023, Vol. 42 Issue 7, pi-xix. 1p.
Autor:
Adrien Bartoli, Andrea Fusiello
The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow,