Tomas Hodan

Tomas Hodan

Research Scientist at Meta

About me

I am a Research Scientist at Meta Reality Labs in Zurich. My research is focused on computer vision, mostly on topics around spatial AI such as detection, pose estimation, tracking, and reconstruction of objects and hands. Prior to joining Meta in 2020, I received my PhD under the supervision of Prof. Jiri Matas at the Czech Technical University in Prague. During my PhD, I visited Microsoft Research in Redmond and Google in Munich as an intern. Prior to that, I obtained my Bachelor’s and Master’s (with honours) degree in computer science from the Brno University of Technology. I am co-organizing the BOP benchmark and R6D workshops, and serve as a reviewer/area chair at academic conferences. I received the Rector’s Prize for an outstanding PhD thesis and the Dean’s Prize for an excellent Master’s thesis.

Updates

2024/10/03 – 
FoundPose presented at ECCV 2024 in Milan.
2024/09/29 – 
Organized the 9th R6D Workshop at ECCV 2024 in Milan (recording).
2024/06/13 – 
HOT3D dataset released!
2024/05/29 – 
2024/04/04 – 
Invited talk at 47th PR&CV Colloquium in Prague (slides).
2023/10/03 – 
Organized the 8th R6D Workshop at ICCV 2023 in Paris.
2023/07/06 – 
2023/02/27 – 
Two papers accepted to CVPR 2023 (In-hand 3D object scanning and AssemblyHands).
2023/02/25 – 
BOP'22 report is on arXiv and will be published at CV4MR workshop at CVPR 2023.
2023/01/16 – 
2022/07/08 – 
Neural Correspondence Field accepted to ECCV 2022 in Tel-Aviv.
2022/05/01 – 
2022/05/01 – 
Organizing the 7th R6D Workshop at ECCV 2022 in Tel-Aviv.
2022/03/01 – 
LISA, our neural hand model, accepted to CVPR 2022 in New Orleans.
2021/07/07 – 
PhD defended! A recording of the defense is available on YouTube.
2020/11/02 – 
Joining Facebook Reality Labs in Redmond as a Research Scientist.
2020/10/02 – 
Code and pre-trained models of EPOS are available on GitHub.
2020/09/15 – 
An analysis of the BOP Challenge 2020 results is now available in an ECCVW 2020 paper.
2020/07/12 – 
2020/07/03 – 
2020/06/05 – 
BOP Challenge 2020 announced, together with BlenderProc4BOP.
2020/04/01 – 
EPOS, our CVPR 2020 paper, is now available on arXiv.org.
2020/02/16 – 
Organizing the 6th R6D Workshop at ECCV 2020 in Glasgow.
2019/11/17 – 
2019/07/26 – 
BOP Challenge 2019 has been opened.
2019/05/20 – 
Invited talk at Siemens AG Research & Technology Center in Munich.
2019/05/01 – 
Organizing the 5th R6D Workshop at ICCV 2019 in Seoul.
2019/04/30 – 
Our paper with Microsoft Research accepted to ICIP 2019 in Taipei.
2018/11/19 – 
Internship at Google in Munich, working with Stefan Hinterstoisser.
2018/09/09 – 
Organizing the 4th R6D Workshop at ECCV 2018 in Munich.
2018/07/03 – 
Our BOP paper accepted to ECCV 2018 in Munich.
2018/06/04 – 
Internship at MSR in Redmond, with Vibhav Vineet, Sudipta Sinha, Brian Guenter.
2017/08/11 – 
Colloquium talk about 6D object pose estimation at the Tampere University.
2017/05/06 – 
Organizing the 3rd R6D Workshop at ICCV 2017 in Venice.
2017/03/28 – 
T-LESS presented at WACV 2017 in Santa Rosa.
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Publications

See my Google Scholar profile for a full list.

HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
Prithviraj Banerjee, Sindi Shkodrani, Pierre Moulon, Shreyas Hampali, Shangchen Han, Fan Zhang, Linguang Zhang, Jade Fountain, Edward Miller, Selen Basol, Richard Newcombe, Robert Wang, Jakob Julian Engel, Tomas Hodan
arXiv 2024
Paper Web
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
Sammy Christen, Shreyas Hampali, Fadime Sener, Edoardo Remelli, Tomas Hodan, Eric Sauser, Shugao Ma, Bugra Tekin
SIGGRAPH Asia 2024, Tokyo
Paper Web
FoundPose: Unseen Object Pose Estimation with Foundation Features
Evin Pınar Örnek, Yann Labbé, Bugra Tekin, Lingni Ma, Cem Keskin, Christian Forster, Tomas Hodan
ECCV 2024, Milan
Paper Web Code
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
Tomas Hodan, Martin Sundermeyer, Yann Labbé, Van Nguyen Nguyen, Gu Wang, Eric Brachmann, Bertram Drost, Vincent Lepetit, Carsten Rother, Jiri Matas
CVPR Workshops (CV4MR) 2024, Seattle
Paper Web
CNOS: A Strong Baseline for CAD-based Novel Object Segmentation
Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Vincent Lepetit, Tomas Hodan
ICCV Workshops (R6D) 2023, Vancouver
Paper Code
In-Hand 3D Object Scanning from an RGB Sequence
Shreyas Hampali, Tomas Hodan, Luan Tran, Lingni Ma, Cem Keskin, Vincent Lepetit
CVPR 2023, Vancouver
Paper Web Video BibTeX
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
Takehiko Ohkawa, Kun He, Fadime Sener, Tomas Hodan, Luan Tran, Cem Keskin
CVPR 2023, Vancouver
Paper Web
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
Martin Sundermeyer, Tomas Hodan, Yann Labbé, Gu Wang, Eric Brachmann, Bertram Drost, Carsten Rother, Jiri Matas
CVPR Workshops (CV4MR) 2023, Vancouver
Paper Web BibTeX
UmeTrack: Unified Multi-View End-to-End Hand Tracking for VR
Shangchen Han, Po-chen Wu, Yubo Zhang, Beibei Liu, Linguang Zhang, Zheng Wang, Weiguang Si, Peizhao Zhang, Yujun Cai, Tomas Hodan, Randi Cabezas, Luan Tran, Muzaffer Akbay, Tsz-Ho Yu, Cem Keskin, Robert Wang
SIGGRAPH Asia 2022, Daegu
Paper Code
Neural Correspondence Field for Object Pose Estimation
Lin Huang, Tomas Hodan, Lingni Ma, Linguang Zhang, Luan Tran, Christopher Twigg, Po-Chen Wu, Junsong Yuan, Cem Keskin, Robert Wang
ECCV 2022, Tel-Aviv
Paper Code Web BibTeX
LISA: Learning Implicit Shape and Appearance of Hands
Enric Corona, Tomas Hodan, Minh Vo, Francesc Moreno-Noguer, Chris Sweeney, Richard Newcombe, Lingni Ma
CVPR 2022, New Orleans
Paper Web BibTeX
BOP Challenge 2020 on 6D Object Localization
Tomas Hodan, Martin Sundermeyer, Bertram Drost, Yann Labbé, Eric Brachmann, Frank Michel, Carsten Rother, Jiri Matas
ECCV Workshops (R6D) 2020, Glasgow
Paper Web Workshop BibTeX
Learning Surrogates via Deep Embedding
Yash Patel, Tomas Hodan, Jiri Matas
ECCV 2020, Glasgow
Paper Video BibTeX
BlenderProc: Reducing the Reality Gap with Photorealistic Rendering
Maximilian Denninger, Martin Sundermeyer, Dominik Winkelbauer, Dmitry Olefir, Tomas Hodan, Youssef Zidan, Mohamad Elbadrawy, Markus Knauer, Harinandan Katam, Ahsan Lodhi
RSS Workshops 2020, Corvallis
Paper Code Video BibTeX
EPOS: Estimating 6D Pose of Objects with Symmetries
Tomas Hodan, Daniel Barath, Jiri Matas
CVPR 2020, Seattle
Paper Code Web Video Demo BibTeX
Photorealistic Image Synthesis for Object Instance Detection
Tomas Hodan, Vibhav Vineet, Ran Gal, Emanuel Shalev, Jon Hanzelka, Treb Connell, Pedro Urbina, Sudipta N. Sinha, Brian Guenter
ICIP 2019, Taipei
Paper Dataset Slides BibTeX
BOP: Benchmark for 6D Object Pose Estimation
Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
ECCV 2018, Munich
Paper Web Slides Poster BibTeX
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
Tomas Hodan, Pavel Haluza, Stepan Obdrzalek, Jiri Matas, Manolis Lourakis, Xenophon Zabulis
WACV 2017, Santa Rosa
Paper Web Slides Poster BibTeX
On Evaluation of 6D Object Pose Estimation
Tomas Hodan, Jiri Matas, Stepan Obdrzalek
ECCV Workshops 2016, Amsterdam
Paper Slides Code BibTeX
Detection and Fine 3D Pose Estimation of Texture-less Objects in RGB-D Images
Tomas Hodan, Xenophon Zabulis, Manolis Lourakis, Stepan Obdrzalek, Jiri Matas
IROS 2015, Hamburg
Paper Slides Video BibTeX
Efficient Texture-less Object Detection for Augmented Reality Guidance
Tomas Hodan, Dima Damen, Walterio Mayol-Cuevas, Jiri Matas
ISMAR Workshops 2015, Fukuoka
Paper BibTeX

PhD thesis

Pose Estimation of Specific Rigid Objects
Supervisor: Prof. Jiri Matas
Reviewers: Prof. Vincent Lepetit, Prof. Markus Vincze, Dr. Slobodan Ilic
Czech Technical University in Prague, 2021
Received the Rector's Prize for an outstanding PhD thesis
Thesis Defense Slides BibTeX

Activities

Co-organized BOP challenges on object pose estimation:

Co-organized R6D workshops on object pose estimation: