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of 78
pro vyhledávání: '"KARADENİZ, Ahmet"'
Autor:
Karadeniz, Ahmet Serdar, Mallis, Dimitrios, Mejri, Nesryne, Cherenkova, Kseniya, Kacem, Anis, Aouada, Djamila
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images. By jointly learning both outputs, DAVINCI minimizes error accumulatio
Externí odkaz:
http://arxiv.org/abs/2410.22857
Autor:
Karadeniz, Ahmet Serdar, Mallis, Dimitrios, Mejri, Nesryne, Cherenkova, Kseniya, Kacem, Anis, Aouada, Djamila
We propose PICASSO, a novel framework CAD sketch parameterization from hand-drawn or precise sketch images via rendering self-supervision. Given a drawing of a CAD sketch, the proposed framework turns it into parametric primitives that can be importe
Externí odkaz:
http://arxiv.org/abs/2407.13394
Autor:
Mallis, Dimitrios, Ali, Sk Aziz, Dupont, Elona, Cherenkova, Kseniya, Karadeniz, Ahmet Serdar, Khan, Mohammad Sadil, Kacem, Anis, Gusev, Gleb, Aouada, Djamila
Recent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processes and relating them to real objects. In this context, 3D reverse engin
Externí odkaz:
http://arxiv.org/abs/2308.15966
Reconstructing 3D human body shapes from 3D partial textured scans remains a fundamental task for many computer vision and graphics applications -- e.g., body animation, and virtual dressing. We propose a new neural network architecture for 3D body s
Externí odkaz:
http://arxiv.org/abs/2208.08768
Autor:
Karadeniz, Ahmet Mehmet1 (AUTHOR) karadeniz.ahmet.mehmet@sze.hu, Ballagi, Áron2 (AUTHOR) ballagi@sze.hu, Kóczy, László T.3 (AUTHOR) koczy@sze.hu
Publikováno v:
Symmetry (20738994). Sep2024, Vol. 16 Issue 9, p1180. 19p.
Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional enhancement techniques almost impossible to apply. Recently, learning-b
Externí odkaz:
http://arxiv.org/abs/2006.09845
Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional image enhancement techniques almost impossible to apply. Very recently,
Externí odkaz:
http://arxiv.org/abs/2003.07823
Steering angle prediction in autonomous vehicles: a deep learning approach combining VGG16 and LSTM.
Publikováno v:
Journal of Computer & Electrical & Electronics Engineering Sciences (JCEEES); 2024, Vol. 2 Issue 2, p46-51, 6p
Autor:
Özkaya Toraman, Kübra, Meral, Rasim, Karadeniz, Ahmet Nafiz, Kaval, Gizem, Başaran, Mert, Ekenel, Meltem, Altun, Musa
Publikováno v:
Journal of Chemotherapy (Taylor & Francis Ltd); Apr2024, Vol. 36 Issue 2, p133-142, 10p
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