Zobrazeno 1 - 10
of 291
pro vyhledávání: '"Rojas, Sara"'
Autor:
S, Gabriel Pérez, Pérez, Juan C., Alfarra, Motasem, Zarzar, Jesús, Rojas, Sara, Ghanem, Bernard, Arbeláez, Pablo
This paper presents preliminary work on a novel connection between certified robustness in machine learning and the modeling of 3D objects. We highlight an intriguing link between the Maximal Certified Radius (MCR) of a classifier representing a spac
Externí odkaz:
http://arxiv.org/abs/2408.13135
Autor:
Mai, Jinjie, Zhu, Wenxuan, Rojas, Sara, Zarzar, Jesus, Hamdi, Abdullah, Qian, Guocheng, Li, Bing, Giancola, Silvio, Ghanem, Bernard
Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy. Previous solutions for learning NeRFs with s
Externí odkaz:
http://arxiv.org/abs/2408.10739
Autor:
Rojas, Sara, Philip, Julien, Zhang, Kai, Bi, Sai, Luan, Fujun, Ghanem, Bernard, Sunkavall, Kalyan
Publikováno v:
ECCV 2024
Recent advancements in diffusion models have shown remarkable proficiency in editing 2D images based on text prompts. However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can
Externí odkaz:
http://arxiv.org/abs/2404.04526
Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across computer vision applications, their potential remains underexplored when lea
Externí odkaz:
http://arxiv.org/abs/2306.08904
Autor:
Rojas, Sara, Zarzar, Jesus, Perez, Juan Camilo, Sanakoyeu, Artsiom, Thabet, Ali, Pumarola, Albert, Ghanem, Bernard
This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling Real-time Rendering of NeRFs across Devices. Re-ReND is designed to ac
Externí odkaz:
http://arxiv.org/abs/2303.08717
Recent advances in Neural Radiance Fields (NeRF) boast impressive performances for generative tasks such as novel view synthesis and 3D reconstruction. Methods based on neural radiance fields are able to represent the 3D world implicitly by relying e
Externí odkaz:
http://arxiv.org/abs/2211.11215
Autor:
Sanchez-Cano, Gabriel, Cristobal-Cueto, Pablo, Nuño-Ortega, Paula, Sáez, Lydia, Lastra, Antonio, Rojas, Sara, Horcajada, Patricia
Publikováno v:
In Journal of Environmental Chemical Engineering April 2024 12(2)
Autor:
Habboush, Shayma1 (AUTHOR) shaymahabboush@correo.ugr.es, Rojas, Sara2 (AUTHOR) srojas@ugr.es, Rodríguez, Noel1 (AUTHOR) noel@ugr.es, Rivadeneyra, Almudena1 (AUTHOR) arivadeneyra@ugr.es
Publikováno v:
Sensors (14248220). May2024, Vol. 24 Issue 9, p2717. 35p.
Publikováno v:
ECCV 2020
Deep neural networks are vulnerable to adversarial attacks, in which imperceptible perturbations to their input lead to erroneous network predictions. This phenomenon has been extensively studied in the image domain, and has only recently been extend
Externí odkaz:
http://arxiv.org/abs/1912.00461
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