FabianManhardt

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

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 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).
  • 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 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

Running
DA/MA/BAPhotorealistic 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)

Finished
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

2020
F. Manhardt, Manuel Nickel, Sven Meier, Luca Minciullo, N. Navab
CPS: Class-level 6D Pose and Shape Estimation From Monocular Images
arXiv/2003.05848v1, March 2020 (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: Class-level 6D Pose and Shape Estimation From Monocular Images

CPS In this paper, we propose the first deep learning approach for class-wise monocular 6D pose estimation, coupled with metric shape retrieval. We propose 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.


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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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