Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Drost, Bertram"'
Autor:
Hodan, Tomas, Sundermeyer, Martin, Labbe, Yann, Nguyen, Van Nguyen, Wang, Gu, Brachmann, Eric, Drost, Bertram, Lepetit, Vincent, Rother, Carsten, Matas, Jiri
We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related
Externí odkaz:
http://arxiv.org/abs/2403.09799
Autor:
Sundermeyer, Martin, Hodan, Tomas, Labbe, Yann, Wang, Gu, Brachmann, Eric, Drost, Bertram, Rother, Carsten, Matas, Jiri
We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image.
Externí odkaz:
http://arxiv.org/abs/2302.13075
Autor:
König, Rebecca, Drost, Bertram
We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art deep learning based instance detector to segment object instances in an RGB image, followed by a point-pair based voting method to recover the object's pose. W
Externí odkaz:
http://arxiv.org/abs/2011.05669
Autor:
Hodan, Tomas, Sundermeyer, Martin, Drost, Bertram, Labbe, Yann, Brachmann, Eric, Michel, Frank, Rother, Carsten, Matas, Jiri
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D im
Externí odkaz:
http://arxiv.org/abs/2009.07378
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
Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly. One possibility is to use a d
Externí odkaz:
http://arxiv.org/abs/1807.02001
Publikováno v:
In Computer Vision and Image Understanding January 2018 166:66-80
Autor:
Drost, Bertram H.1 drost@mvtec.com, Ilic, Slobodan2 Slobodan.Ilic@siemens.com
Publikováno v:
Machine Vision & Applications. Feb2018, Vol. 29 Issue 2, p299-311. 13p.
Autor:
K��nig, Rebecca, Drost, Bertram
We propose a method for 6DoF pose estimation of rigid objects that uses a state-of-the-art deep learning based instance detector to segment object instances in an RGB image, followed by a point-pair based voting method to recover the object's pose. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0db88f42dffc38fcc1e766bcfdbc3d7
http://arxiv.org/abs/2011.05669
http://arxiv.org/abs/2011.05669