Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Boyer, Edmond"'
Volumetric shape representations have become ubiquitous in multi-view reconstruction tasks. They often build on regular voxel grids as discrete representations of 3D shape functions, such as SDF or radiance fields, either as the full shape model or a
Externí odkaz:
http://arxiv.org/abs/2407.19837
Autor:
Pesavento, Marco, Xu, Yuanlu, Sarafianos, Nikolaos, Maier, Robert, Wang, Ziyan, Yao, Chun-Han, Volino, Marco, Boyer, Edmond, Hilton, Adrian, Tung, Tony
Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover
Externí odkaz:
http://arxiv.org/abs/2403.10357
Autor:
Wang, Angtian, Xu, Yuanlu, Sarafianos, Nikolaos, Maier, Robert, Boyer, Edmond, Yuille, Alan, Tung, Tony
Neural reconstruction and rendering strategies have demonstrated state-of-the-art performances due, in part, to their ability to preserve high level shape details. Existing approaches, however, either represent objects as implicit surface functions o
Externí odkaz:
http://arxiv.org/abs/2312.17192
Autor:
Li, Jianning, Zhou, Zongwei, Yang, Jiancheng, Pepe, Antonio, Gsaxner, Christina, Luijten, Gijs, Qu, Chongyu, Zhang, Tiezheng, Chen, Xiaoxi, Li, Wenxuan, Wodzinski, Marek, Friedrich, Paul, Xie, Kangxian, Jin, Yuan, Ambigapathy, Narmada, Nasca, Enrico, Solak, Naida, Melito, Gian Marco, Vu, Viet Duc, Memon, Afaque R., Schlachta, Christopher, De Ribaupierre, Sandrine, Patel, Rajnikant, Eagleson, Roy, Chen, Xiaojun, Mächler, Heinrich, Kirschke, Jan Stefan, de la Rosa, Ezequiel, Christ, Patrick Ferdinand, Li, Hongwei Bran, Ellis, David G., Aizenberg, Michele R., Gatidis, Sergios, Küstner, Thomas, Shusharina, Nadya, Heller, Nicholas, Andrearczyk, Vincent, Depeursinge, Adrien, Hatt, Mathieu, Sekuboyina, Anjany, Löffler, Maximilian, Liebl, Hans, Dorent, Reuben, Vercauteren, Tom, Shapey, Jonathan, Kujawa, Aaron, Cornelissen, Stefan, Langenhuizen, Patrick, Ben-Hamadou, Achraf, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Bolelli, Federico, Grana, Costantino, Lumetti, Luca, Salehi, Hamidreza, Ma, Jun, Zhang, Yao, Gharleghi, Ramtin, Beier, Susann, Sowmya, Arcot, Garza-Villarreal, Eduardo A., Balducci, Thania, Angeles-Valdez, Diego, Souza, Roberto, Rittner, Leticia, Frayne, Richard, Ji, Yuanfeng, Ferrari, Vincenzo, Chatterjee, Soumick, Dubost, Florian, Schreiber, Stefanie, Mattern, Hendrik, Speck, Oliver, Haehn, Daniel, John, Christoph, Nürnberger, Andreas, Pedrosa, João, Ferreira, Carlos, Aresta, Guilherme, Cunha, António, Campilho, Aurélio, Suter, Yannick, Garcia, Jose, Lalande, Alain, Vandenbossche, Vicky, Van Oevelen, Aline, Duquesne, Kate, Mekhzoum, Hamza, Vandemeulebroucke, Jef, Audenaert, Emmanuel, Krebs, Claudia, van Leeuwen, Timo, Vereecke, Evie, Heidemeyer, Hauke, Röhrig, Rainer, Hölzle, Frank, Badeli, Vahid, Krieger, Kathrin, Gunzer, Matthias, Chen, Jianxu, van Meegdenburg, Timo, Dada, Amin, Balzer, Miriam, Fragemann, Jana, Jonske, Frederic, Rempe, Moritz, Malorodov, Stanislav, Bahnsen, Fin H., Seibold, Constantin, Jaus, Alexander, Marinov, Zdravko, Jaeger, Paul F., Stiefelhagen, Rainer, Santos, Ana Sofia, Lindo, Mariana, Ferreira, André, Alves, Victor, Kamp, Michael, Abourayya, Amr, Nensa, Felix, Hörst, Fabian, Brehmer, Alexander, Heine, Lukas, Hanusrichter, Yannik, Weßling, Martin, Dudda, Marcel, Podleska, Lars E., Fink, Matthias A., Keyl, Julius, Tserpes, Konstantinos, Kim, Moon-Sung, Elhabian, Shireen, Lamecker, Hans, Zukić, Dženan, Paniagua, Beatriz, Wachinger, Christian, Urschler, Martin, Duong, Luc, Wasserthal, Jakob, Hoyer, Peter F., Basu, Oliver, Maal, Thomas, Witjes, Max J. H., Schiele, Gregor, Chang, Ti-chiun, Ahmadi, Seyed-Ahmad, Luo, Ping, Menze, Bjoern, Reyes, Mauricio, Deserno, Thomas M., Davatzikos, Christos, Puladi, Behrus, Fua, Pascal, Yuille, Alan L., Kleesiek, Jens, Egger, Jan
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit s
Externí odkaz:
http://arxiv.org/abs/2308.16139
Autor:
Armando, Matthieu, Boissieux, Laurence, Boyer, Edmond, Franco, Jean-Sebastien, Humenberger, Martin, Legras, Christophe, Leroy, Vincent, Marsot, Mathieu, Pansiot, Julien, Pujades, Sergi, Rekik, Rim, Rogez, Gregory, Swamy, Anilkumar, Wuhrer, Stefanie
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain different actors wearing different outfits while performing different
Externí odkaz:
http://arxiv.org/abs/2306.07399
Autor:
Ben-Hamadou, Achraf, Smaoui, Oussama, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Lim, Hoyeon, Kim, Minchang, Lee, Minkyung, Chung, Minyoung, Shin, Yeong-Gil, Leclercq, Mathieu, Cevidanes, Lucia, Prieto, Juan Carlos, Zhuang, Shaojie, Wei, Guangshun, Cui, Zhiming, Zhou, Yuanfeng, Dascalu, Tudor, Ibragimov, Bulat, Yong, Tae-Hoon, Ahn, Hong-Gi, Kim, Wan, Han, Jae-Hwan, Choi, Byungsun, van Nistelrooij, Niels, Kempers, Steven, Vinayahalingam, Shankeeth, Strippoli, Julien, Thollot, Aurélien, Setbon, Hugo, Trosset, Cyril, Ladroit, Edouard
Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms f
Externí odkaz:
http://arxiv.org/abs/2305.18277
Autor:
Ben-Hamadou, Achraf, Smaoui, Oussama, Chaabouni-Chouayakh, Houda, Rekik, Ahmed, Pujades, Sergi, Boyer, Edmond, Strippoli, Julien, Thollot, Aurélien, Setbon, Hugo, Trosset, Cyril, Ladroit, Edouard
Teeth segmentation and labeling are critical components of Computer-Aided Dentistry (CAD) systems. Indeed, before any orthodontic or prosthetic treatment planning, a CAD system needs to first accurately segment and label each instance of teeth visibl
Externí odkaz:
http://arxiv.org/abs/2210.06094
In this paper, we investigate a new optimization framework for multi-view 3D shape reconstructions. Recent differentiable rendering approaches have provided breakthrough performances with implicit shape representations though they can still lack prec
Externí odkaz:
http://arxiv.org/abs/2209.00082
Vertebrae localization, segmentation and identification in CT images is key to numerous clinical applications. While deep learning strategies have brought to this field significant improvements over recent years, transitional and pathological vertebr
Externí odkaz:
http://arxiv.org/abs/2110.12177
Autor:
Regateiro, Joao, Boyer, Edmond
We consider the problem of modifying/replacing the shape style of a real moving character with those of an arbitrary static real source character. Traditional solutions follow a pose transfer strategy, from the moving character to the source characte
Externí odkaz:
http://arxiv.org/abs/2109.01587