Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Horache, Sofiane"'
Autor:
Horache, Sofiane, Deschaud, Jean-Emmanuel, Goulette, François, Gruel, Katherine, Lejars, Thierry, Masson, Olivier
Clustering coins with respect to their die is an important component of numismatic research and crucial for understanding the economic history of tribes (especially when literary production does not exist, in celtic culture). It is a very hard task t
Externí odkaz:
http://arxiv.org/abs/2109.15033
We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MS-SVConv is a fast deep neural netw
Externí odkaz:
http://arxiv.org/abs/2103.14533
We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on3D data. Its modular design, efficient implementation, and user-friendly interfaces make it a relevant tool for research and productization alike.
Externí odkaz:
http://arxiv.org/abs/2010.04642
Autor:
Horache, Sofiane, Deschaud, Jean-Emmanuel, Goulette, François, Gruel, Katherine, Lejars, Thierry
The recognition and clustering of coins which have been struck by the same die is of interest for archeological studies. Nowadays, this work can only be performed by experts and is very tedious. In this paper, we propose a method to automatically clu
Externí odkaz:
http://arxiv.org/abs/2005.05705
Autor:
Colombo, Lorenzo, Oumani, Ayoub, Schmidt-Mengin, Marius, Horache, Sofiane, Romdhani, Sami, Kandiban, Sanmady, Romain, Blandine, Temiz, Gizem, Teboul, Olivier, Paragios, Nikos, Fenoglietto, Pascal
Publikováno v:
In Radiotherapy and Oncology May 2024 194 Supplement 1:S1288-S1290
Autor:
Colombo, Lorenzo, Oumani, Ayoub, Schmidt-Mengin, Marius, Horache, Sofiane, Romdhani, Sami, Kandiban, Sanmady, Romain, Blandine, Temiz, Gizem, Teboul, Olivier, Paragios, Nikos, Fenoglietto, Pascal
Publikováno v:
In Radiotherapy and Oncology May 2024 194 Supplement 1:S545-S547
We present MS-SVConv, a fast multi-scale deep neural network that outputs features from point clouds for 3D registration between two scenes. We compute features using a 3D sparse voxel convolutional network on a point cloud at different scales and th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::051fdc8fa98bbc6a5da24bc91f563761
https://hal-mines-paristech.archives-ouvertes.fr/hal-03203390
https://hal-mines-paristech.archives-ouvertes.fr/hal-03203390
Autor:
Colombo, Lorenzo (AUTHOR), Oumani, Ayoub (AUTHOR), Schmidt-Mengin, Marius (AUTHOR), Horache, Sofiane (AUTHOR), Romdhani, Sami (AUTHOR), Kandiban, Sanmady (AUTHOR), Romain, Blandine (AUTHOR), Temiz, Gizem (AUTHOR), Teboul, Olivier (AUTHOR), Paragios, Nikos (AUTHOR), Fenoglietto, Pascal (AUTHOR)
Publikováno v:
Radiotherapy & Oncology. 2024 Supplement 1, Vol. 194, pS1659-S1661. 3p.