Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Emanuele Rodolà"'
Publikováno v:
International Journal of Computer Vision. 130:1474-1493
The Average Mixing Kernel Signature is a novel spectral signature for points on non-rigid three-dimensional shapes. It is based on a quantum exploration process of the shape surface, where the average transition probabilities between the points of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e8962a687549ac7ef72d10fe8437b12
https://doi.org/10.1007/s11263-022-01610-y
https://doi.org/10.1007/s11263-022-01610-y
Autor:
Babak Solhjoo, Emanuele Rodolà
Publikováno v:
Proceedings of the 15th International Conference on Agents and Artificial Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06629b69c1e15edcc9717ef43cdaef20
https://doi.org/10.5220/0011860200003393
https://doi.org/10.5220/0011860200003393
Publikováno v:
ACM SIGGRAPH 2022 Posters.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::180045c04079a37aa63ba2170645f7c6
https://doi.org/10.1145/3532719.3543211
https://doi.org/10.1145/3532719.3543211
Publikováno v:
ACM Transactions on Graphics
We introduce a novel computational framework for digital geometry processing, based upon the derivation of a nonlinear operator associated to the total variation functional. Such operator admits a generalized notion of spectral decomposition, yieldin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::559134b24a2a25d5bdbe162d2a3a26be
https://doi.org/10.1145/3414685.3417849
https://doi.org/10.1145/3414685.3417849
Autor:
Cristiano Massaroni, Luigi Cinque, Marco Cascio, Gian Luca Foresti, Danilo Avola, Emanuele Rodolà
Publikováno v:
IEEE Transactions on Multimedia. 22:2481-2496
Action recognition in video sequences is an interesting field for many computer vision applications, including behavior analysis, event recognition, and video surveillance. In this article, a method based on 2D skeleton and two-branch stacked Recurre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05abd3402d15ef299f173108568c5095
https://doi.org/10.1109/tmm.2019.2960588
https://doi.org/10.1109/tmm.2019.2960588
Publikováno v:
Computer Graphics Forum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d67215b14c16827faef05ef26bf1e8d9
https://doi.org/10.1111/cgf.14083
https://doi.org/10.1111/cgf.14083
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197680
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d5d4d443a0444faa47ea2ca6498cca5
https://doi.org/10.1007/978-3-031-19769-7_10
https://doi.org/10.1007/978-3-031-19769-7_10
Autor:
Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà
Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape. Some recent w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47d9e7b6189b6331944ae75c2f1fc57b
https://hdl.handle.net/10278/5002939
https://hdl.handle.net/10278/5002939
Publikováno v:
ATHENA Research Book, Volume 1 ISBN: 9789612866587
Estimating the depth of a scene from a single image is impossible due to scale ambiguity, but recent deep learning approaches have demonstrated to produce faithful estimates anyways by learning the typical scale of objects from implicit clues in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61dc9e394d9c0e28218b66011aca1328
https://doi.org/10.18690/um.3.2022.3
https://doi.org/10.18690/um.3.2022.3
Autor:
Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà
Publikováno v:
Scopus-Elsevier
Sapienza Università di Roma-IRIS
Sapienza Università di Roma-IRIS
In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed approach is data-driven and adopts for the first time the transformer architecture in the registration task. Our method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58c9723a839afbc564378a6dcc11412d
http://arxiv.org/abs/2106.13679
http://arxiv.org/abs/2106.13679