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pro vyhledávání: '"Sahin, Caner"'
Object pose recovery has gained increasing attention in the computer vision field as it has become an important problem in rapidly evolving technological areas related to autonomous driving, robotics, and augmented reality. Existing review-related st
Externí odkaz:
http://arxiv.org/abs/2001.10609
6D object pose estimation is an important task that determines the 3D position and 3D rotation of an object in camera-centred coordinates. By utilizing such a task, one can propose promising solutions for various problems related to scene understandi
Externí odkaz:
http://arxiv.org/abs/1903.04229
Autor:
Avnioglu, Seda, Sahin, Caner, Cankaya, Seyda, Ozen, Ozkan, Dikici, Rumeysa, Yilmaz, Halil, Velioglu, Halil Aziz, Yulug, Burak
Publikováno v:
In Journal of Psychiatric Research April 2023 160:86-92
Autor:
Hodan, Tomas, Kouskouridas, Rigas, Kim, Tae-Kyun, Tombari, Federico, Bekris, Kostas, Drost, Bertram, Groueix, Thibault, Walas, Krzysztof, Lepetit, Vincent, Leonardis, Ales, Steger, Carsten, Michel, Frank, Sahin, Caner, Rother, Carsten, Matas, Jiri
This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and
Externí odkaz:
http://arxiv.org/abs/1810.03758
Autor:
Hodan, Tomas, Michel, Frank, Brachmann, Eric, Kehl, Wadim, Buch, Anders Glent, Kraft, Dirk, Drost, Bertram, Vidal, Joel, Ihrke, Stephan, Zabulis, Xenophon, Sahin, Caner, Manhardt, Fabian, Tombari, Federico, Kim, Tae-Kyun, Matas, Jiri, Rother, Carsten
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets i
Externí odkaz:
http://arxiv.org/abs/1808.08319
Autor:
Sahin, Caner, Kim, Tae-Kyun
Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks. In this study, we address category-level full 6D object pose estimation in the context of depth modality, introducing a nove
Externí odkaz:
http://arxiv.org/abs/1808.00255
In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe occlusion, clutter, and similar-looking distractors. Most existing metho
Externí odkaz:
http://arxiv.org/abs/1806.03891
Autor:
Sahin, Caner, Kim, Tae-Kyun
A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modalit
Externí odkaz:
http://arxiv.org/abs/1706.03285
State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduce a novel architecture called Iterative Hough Forest with Histogram of
Externí odkaz:
http://arxiv.org/abs/1701.02166
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we introduce a novel architecture called Iterative Hough Forest with Histogr
Externí odkaz:
http://arxiv.org/abs/1603.02617