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pro vyhledávání: '"Ilg, A"'
We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruction using
Externí odkaz:
http://arxiv.org/abs/2408.16544
Autor:
Allen, Julia, Alves, Bruno, Arling, Jan-Hendrik, Augsten, Kamil, Bagnaschi, Emanuele, Benato, Giovanni, Bennecke, Anna, Borca, Cecilia, Braz, Paulo, Brenner, Lydia, Degens, Jordy, Dengler, Yannick, Dimitriadi, Christina, Diociaiuti, Eleonora, Dufour, Laurent, Dunne, Patrick, Etisken, Ozgur, Ravasio, Silvia Ferrario, Fomin, Nikolai, Alonso, Andrea Garcia, Gellersen, Leif, Gsponer, Andreas, Herman, Tomas, Hiti, Bojan, Huhta, Laura, Ilg, Armin, Jarkovská, Kateřina, Jovicevic, Jelena, Keszeghova, Lucia, Kirschenmann, Henning, Klaver, Suzanne, Korajac, Arman, Kotsokechagia, Anastasia, Kussner, Meike, Lelek, Aleksandra, Lospalluto, Guiseppe, Major, Péter, Maksimovic, Veljko, Malczewski, Jakub, Benito, Carla Marin, Suarez, Paula Martinez, Milosevic, Vukasin, Modak, Atanu, Tarda, Arnau Morancho, Valero, Laura Moreno, Niel, Elisabeth, Nikiforou, Nikiforos, Novosel, Anja, Paakkinen, Petja, Pacey, Holly, Pedro, Rute, Pesut, Marko, Pietrzyk, Guillaume, Pitt, Michael, Placinta, Vlad-Mihai, Dash, Archita Rani, Räuber, Géraldine, Shopova, Mariana, Someonov, Radoslav, Simsek, Sinem, Skovpen, Kirill, Sopkova, Filomena, Souza, Fernando, Norella, Elisabetta Spadaro, Urbaniak, Marta, Gomez, Lourdes Urda, Wallin, Erik, Zaccolo, Valentina, Zardoshti, Nima, Zarnecki, Grzegorz
The European Committee for Future Accelerators (ECFA) Early-Career Researcher (ECR) panel, which represents the interests of the ECR community to ECFA, presents in this document its initiatives and activities in the year 2023. This report summarises
Externí odkaz:
http://arxiv.org/abs/2407.12761
Autor:
Fischer, Tom, Liu, Yaoyao, Jesslen, Artur, Ahmed, Noor, Kaushik, Prakhar, Wang, Angtian, Yuille, Alan, Kortylewski, Adam, Ilg, Eddy
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending th
Externí odkaz:
http://arxiv.org/abs/2407.09271
Publikováno v:
Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22787-22796
Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far methods that estimate the category-level object pose require either lar
Externí odkaz:
http://arxiv.org/abs/2407.04384
Since its inception, the Large Hadron Collider (LHC) has significantly advanced particle physics and will continue to do so in the context of the High Luminosity LHC (HL-LHC) program to collect $3000$ fb$^{-1}$ by the end of 2041. The particle physic
Externí odkaz:
http://arxiv.org/abs/2407.01852
Autor:
Blekman, Freya, Canelli, Florencia, De Moor, Alexandre, Gautam, Kunal, Ilg, Armin, Macchiolo, Anna, Ploerer, Eduardo
Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train. The DeepJetTransformer network uses in
Externí odkaz:
http://arxiv.org/abs/2406.08590
Deep learning has revolutionized the field of computer vision by introducing large scale neural networks with millions of parameters. Training these networks requires massive datasets and leads to intransparent models that can fail to generalize. At
Externí odkaz:
http://arxiv.org/abs/2405.14599
Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that ma
Externí odkaz:
http://arxiv.org/abs/2405.10575
Autor:
Allen, Julia, Augsten, Kamil, Benato, Giovanni, Kraljevic, Neven Blaskovic, Brizioli, Francesco, Diociaiuti, Eleonora, Hinger, Viktoria, Ilg, Armin, Jarkovská, Kateřina, Gajdošová, Katarína Křížková, Kuich, Magdalena, Lelek, Aleksandra, Moureaux, Louis, Pacey, Holly, Pietrzyk, Guillaume, Räuber, Géraldine, Ripellino, Giulia, Schramm, Steven, Shopova, Mariana, Sznajder, Pawel, Waldron, Abby
This document presents the outcomes of a comprehensive survey conducted among early career researchers (ECRs) in academic particle physics. Running from September 24, 2022, to March 3, 2023, the survey gathered responses from 759 ECRs employed in 39
Externí odkaz:
http://arxiv.org/abs/2404.02074
We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture. Existing methods for generalizable 3D reconstruction either do not scale to large scene
Externí odkaz:
http://arxiv.org/abs/2403.16292