FabianManhardt

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Fabian Manhardt

Contact

Fabian Manhardt

Fabian Manhardt ORCID iD icon

Email: fabian.manhardt[at]tum.de

Position: PhD Candidate

Address:

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München


Office: MI 03.13.040

News

  • Jul 27 (2021): 2 Papers accepted to ICCV'21
  • Jun 30 (2021): 1 Paper accepted to IROS'21
  • Mar 1 (2021): I'm finished!
  • Mar 1 (2021): 1 Paper accepted to CVPR'21
  • Nov 10 (2020): Our results for CPS++ on NOCS can be downloaded here
  • Nov 6 (2020): Data for self-supervision from CPS++ can be downloaded here
  • Nov 2 (2020): 1 Paper accepted to WACV '21
  • Aug 12 (2020): The code for differential rendering from Self6D can be found here
  • Jul 2 (2020): 1 Paper accepted to ECCV '20 (Oral, ~2% acceptance)
  • Jul 23 (2019): 1 Paper accepted to ICCV '19
  • Feb 25 (2019): 1 Paper accepted to CVPR '19
  • Jan 9 (2019): Inference Code of the ECCV '18 paper can be found here
  • Dec 17 (2018): My talk at ECCV '18 can be found here
  • Jul 3 (2018): 2 Papers accepted to ECCV '18 (1 first author as oral, ~2% acceptance).
  • Oct 31 (2017): Inference Code of the ICCV '17 paper can be found here
  • Aug 5 (2017): Our SSD6D paper at ICCV '17 in Venice just got selected as Oral (~2% acceptance) presentation

Research Topics

I am currently involved in conducting research in the following research areas.

  • Computer Vision.
  • Deep Learning.

Student Projects

If you are interested in projects in the area of Computer Vision and Deep Learning, you are always welcome to drop by my office.

Available
DA/MA/BASelf-supervising monocular 6D object pose estimation
(Fabian Manhardt, Federico Tombari)
Master Thesis3D Scene Understanding Leveraging Scene Graphs
(Helisa Dhamo Fabian Manhardt, Federico Tombari)

Running

Finished
Bachelor ThesisPhotorealistic Rendering of Training Data for Object Detection and Pose Estimation with a Physics Engine
(Fabian Manhardt, Johanna Wald, Federico Tombari)
Master ThesisIncremental Learning For Robotic Grasping
(Fabian Manhardt, Nassir Navab)
Bachelor ThesisPhotorealistic Rendering of Training Data for Object Detection and Pose Estimation with a Physics Engine
(Fabian Manhardt, Johanna Wald, Federico Tombari)
Master ThesisDealing with the ambiguity induced by object symmetry in pose estimation tasks
(Fabian Manhardt, Federico Tombari, Prof. Nassir Navab)
Master ThesisClass-Level Object Detection and Pose Estimation from a Single RGB Image Only
(Fabian Manhardt, Federico Tombari, Prof. Nassir Navab)

Teaching Assistance

List of Publications

2021
P. Wang, F. Manhardt, L. Minciullo, L. Garattoni, S. Meier, N. Navab, B. Busam
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021
The first two authors contributed equally to this paper.
(bib)
H. Dhamo, F. Manhardt, N. Navab, F. Tombari
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs
IEEE International Conference on Computer Vision (ICCV), October 2021
The first two authors contributed equally.
(bib)
Y. Di, F. Manhardt, G. Wang, X. Ji, F. Tombari, N. Navab
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
IEEE International Conference on Computer Vision (ICCV), October 2021 (bib)
G. Wang, F. Manhardt, F. Tombari, X. Ji
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimationi
IEEE Computer Vision and Pattern Recognition (CVPR), June 2021 (bib)
L. Minciullo, F. Manhardt, K. Yoshikawa, S. Meier, F. Tombari, N. Kobori
DB-GAN: Boosting Object Recognition Under Strong Lighting Conditions
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 2021.
The first two authors contributed equally to this paper.
(bib)
2020
F. Manhardt, G. Wang, B. Busam, M. Nickel, S. Meier, L. Minciullo, X. Ji, N. Navab
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
arXiv/2003.05848v3, September 2020 (bib)
G. Wang, F. Manhardt, J. Shao, X. Ji, N. Navab, F. Tombari
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
European Conference On Computer Vision (ECCV), Glasgow, Scotland, August [oral].
The first two authors contributed equally to this paper.
(bib)
2019
F. Manhardt, D. Arroyo, C. Rupprecht, B. Busam, T. Birdal, N. Navab, F. Tombari
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019 (bib)
F. Manhardt, W. Kehl, A. Gaidon
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
IEEE Computer Vision and Pattern Recognition (CVPR), Long Beach, United States, June 2019
The first two authors contributed equally.
(bib)
2018
F. Manhardt, W. Kehl, N. Navab, F. Tombari
Deep Model-Based 6D Pose Refinement in RGB
European Conference On Computer Vision (ECCV), Munich, Germany, September 2018 [oral].
The first two authors contributed equally to this paper.
(bib)
T. Hodan, F. Michel, E. Brachmann, W. Kehl, A. Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T.K. Kim, J. Matas, C. Rother
BOP: Benchmark for 6D Object Pose Estimation
European Conference On Computer Vision (ECCV), Munich, Germany, September 2018. (bib)
2017
W. Kehl, F. Manhardt, F. Tombari, S. Ilic, N. Navab
SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
International Conference on Computer Vision (ICCV), Venice, Italy, October 2017 [oral].
The first two authors contributed equally to this paper.
(bib)
2015
A. Luckow, K. Kennedy, F. Manhardt, E. Djerekarov, A. Apon
Automotive big data: Applications, workloads and infrastructures
International Conference on Big Data (Big Data), Santa Clara, CA, USA, December 2015 (bib)

SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again

ssd6d_out.jpg ssd6d_coffee.jpg

We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only.

Deep Model-Based 6D Pose Refinement in RGB

github github github github

We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model.

ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape

RoI-10D We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval. We propose a novel loss formulation by lifting 2D detection, orientation, and scale estimation into 3D space. Instead of optimizing these quantities separately, the 3D instantiation allows to properly measure the metric misalignment of boxes.

Explaining the Ambiguity of Object Detection and 6D Pose from Visual Data

Pose Ambiguities In this work we propose to explicitly deal with visual ambiguities in 6D pose estimation tasks. Therefore, for each object instance we predict multiple 6D pose outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.

CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning

CPS CPS CPS

We propose a novel method for class-level monocular 6D pose estimation, coupled with metric shape retrieval. We leverage a new loss formulation which directly optimizes over all parameters, i.e. 3D orientation, translation, scale and shape at the same time. Instead of decoupling each parameter, we transform the regressed shape, in the form of a point cloud, to 3D and directly measure its metric misalignment. Unfortunately, acquiring adequate annotations is very time-consuming and labor intensive. To overcome this shortcoming, we additionally propose the idea of synthetic-to-real domain transfer for class-level 6D poses by means of self-supervised learning, which removes the burden of collecting numerous manual annotations. In essence, after training our proposed method fully supervised with synthetic data, we leverage recent advances in differentiable rendering to self-supervise the model with unannotated real RGB-D data to improve latter inference. Data can be downloaded here. Results for CPS++ on NOCS can be downloaded here


UsersForm
Title: M.Sc.
Circumference of your head (in cm):  
Firstname: Fabian
Middlename:  
Lastname: Manhardt
Picture: IMG_1130.jpg
Birthday: 20.10.1992
Nationality: Bavaria
Languages: English, German
Groups: Computer Vision
Expertise: Computer Vision
Position: Scientific Staff
Status: Active
Emailbefore: fabian.manhardt
Emailafter: tum.de
Room: 3.13.040
Telephone: +49 (89) 289 - 19417
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