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Tomáš Hodaň. Research Scientist, Reality Labs at Meta. Curriculum vitae | Google Scholar | LinkedIn | GitHub | Twitter. tomhodan@meta.com. I am a Research Scientist at Reality Labs at Meta in Zurich. My research is focused on computer vision, mostly on 3D tracking of objects and hands. Prior to joining Meta in 2020, I received my PhD under ...
2016. Detection and Fine 3D Pose Estimation of Texture-less Objects in RGB-D Images. T Hodaň, X Zabulis, M Lourakis, Š Obdržálek, J Matas. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2015.
Sep 15, 2020 · BOP Challenge 2020 on 6D Object Localization. Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labbe, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas. 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 ...
- Tomáš Hodaň, Martin Sundermeyer, Bertram Drost, Yann Labbé, Eric Brachmann, Frank Michel, Carsten Ro...
- 2020
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...
no code implementations • 30 Dec 2021 • Tomas Hodan. Second, we present HashMatch, an RGB-D method that slides a window over the input image and searches for a match against templates, which are pre-generated by rendering 3D object models in different orientations.
Web: www.hodan.xyz E-mail: tom.hodan@gmail.com Education PhD degree in computer vision (2013 – 2020, defended in 2021) Czech Technical University in Prague, Faculty of Electrical Engineering Doctoral thesis: Pose Estimation of Specific Rigid Objects Supervisor: Prof. Jiˇrí Matas, reviewers: Prof. Vincent Lepetit, Prof. Markus Vincze, Dr ...
Tomas Hodan, Daniel Barath, Jiri Matas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11703-11712. Abstract. We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image.
