Zobrazeno 1 - 10
of 207
pro vyhledávání: '"ALVAREZ, Federico"'
Autor:
Zioulis, Nikolaos, Albanis, Georgios, Drakoulis, Petros, Alvarez, Federico, Zarpalas, Dimitrios, Daras, Petros
Publikováno v:
IEEE Access, Volume 10, 53928 - 53939, 17 May 2022
In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that simultaneously encode two images. The fusion of early encoder features w
Externí odkaz:
http://arxiv.org/abs/2207.04721
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 183, January 2022, Pages 269-285
Spherical cameras capture scenes in a holistic manner and have been used for room layout estimation. Recently, with the availability of appropriate datasets, there has also been progress in depth estimation from a single omnidirectional image. While
Externí odkaz:
http://arxiv.org/abs/2206.11358
Autor:
Hernández-Peñaloza, Gustavo, Uribe, Silvia, García, Francisco Moreno, Graf, Norbert, Álvarez, Federico
Publikováno v:
In EJC Paediatric Oncology December 2024 4
Autor:
Albanis, Georgios, Zioulis, Nikolaos, Drakoulis, Petros, Alvarez, Federico, Zarpalas, Dimitrios, Daras, Petros
In this work we contribute a distribution shift benchmark for a computer vision task; monocular depth estimation. Our differentiation is the decomposition of the wider distribution shift of uncontrolled testing on in-the-wild data, to three distinct
Externí odkaz:
http://arxiv.org/abs/2112.00432
Autor:
Albanis, Georgios, Zioulis, Nikolaos, Drakoulis, Petros, Gkitsas, Vasileios, Sterzentsenko, Vladimiros, Alvarez, Federico, Zarpalas, Dimitrios, Daras, Petros
Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits
Externí odkaz:
http://arxiv.org/abs/2109.02749
Publikováno v:
In Computer Networks May 2024 244
It has been shown that global scene understanding tasks like layout estimation can benefit from wider field of views, and specifically spherical panoramas. While much progress has been made recently, all previous approaches rely on intermediate repre
Externí odkaz:
http://arxiv.org/abs/2102.03939
Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and post-production. In this work we present a data-driven model that estimates an HDR lighting env
Externí odkaz:
http://arxiv.org/abs/2005.08000
Publikováno v:
In Medicina Clínica (English Edition) 11 August 2023 161(3):113-118