Maximilian Weiherer
maximilian dot weiherer at fau dot de

I am a PhD student at FAU Erlangen-Nürnberg, advised by Prof. Bernhard Egger. I am part of the Cognitive Computer Vision Lab at the Chair of Visual Computing.

My research interests are broadly centered around computer vision, computer graphics, and machine learning. I am particularly interested in neural scene representations and anything related (including classical SSMs and 3DMMs) that could be used to model the world we live in. I also enjoy transferring my research into real-world applications, especially in the medical domain.

I obtained my B.Sc. and M.Sc. in Computer Science from OTH Regensburg in 2019 and 2021, respectively. Before joining FAU, I worked as a Student Researcher at the Regensburg Medical Image Computing (ReMIC) Lab, where I was advised by Prof. Christoph Palm. I also spent some time at Fraunhofer IIS as part of the Image Processing and Medical Engineering Department.

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News
  • [10/2023] One paper accepted to WACV'24.
  • [03/2022] Our paper on 3D statistical shape modeling of the female breast is now published in The Visual Computer.
Research

For a full publication list, see my Google Scholar profile. Representative papers are highlighted.

Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo [arXiv]
Maximilian Weiherer, Finn Klein, Bernhard Egger
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

We introduce a new method to compare SSMs by computing approximate intersections and set-theoretic differences between the allowable shape domains spanned by the models.

Learning the shape of female breasts: an open-access 3D statistical shape model of the female breast built from 110 breast scans [pdf] [arXiv] [model]
Maximilian Weiherer, Andreas Eigenberger, Bernhard Egger, Vanessa Brebant, Lukas Prantl, Christoph Palm
The Visual Computer (Vis. Comput.) 39, 2023

We present the first publicly available 3D statistical shape model of the female breast.

Optically tracked and 3D printed haptic phantom hand for surgical training system [pdf]
Johannes Maier, Maximilian Weiherer, Michaela Huber, Christoph Palm
Quantitative Imaging in Medicine and Surgery (QIMS) 10, 2020

Improved previously developed metamaterials to imitate human soft tissue and presented an optically tracked 3D printed phantom of the whole hand.

Retrospective Color Shading Correction for Endoscopic Images [pdf]
Maximilian Weiherer, Martin Zorn, Thomas Wittenberg, Christoph Palm
Bildverarbeitung für die Medizin (BVM), 2020

Extending the retrospective shading correction method based on signal envelope estimation to color images using principal color components.

Imitating human soft tissue on basis of a dual-material 3D print using a support-filled metamaterial to provide bimanual haptic for a hand surgery training system [pdf]
Johannes Maier, Maximilian Weiherer, Michaela Huber, Christoph Palm
Quantitative Imaging in Medicine and Surgery (QIMS) 9, 2019

We propose new 3D-printable support-filled metamaterials to imitate human soft tissue.

Stitching Pathological Tissue Images using DOP Feature Tracking [pdf]
Matthias Bergler, Maximilian Weiherer, Tobias Bergen, Malte Avenhaus, David Rauber, Thomas Wittenberg, Christian Münzenmayer, Michaela Benz
Bildverarbeitung für die Medizin (BVM), 2018

Using the difference of two elliptical paraboloids as approximation of the Laplacian of Gaussian, we enable real-time image stitching of microscopic images.

Not-So-Serious Research
From Zero to Hero: Convincing with Extremely Complicated Math [pdf] [arXiv] [code]
Maximilian Weiherer, Bernhard Egger
Special Interest Group on Harry Quechua Bovik (SIGBOVIK), 2023

We present zero2hero, an innovative system that automatically extracts embarrassingly simple equations from a given Latex document and makes them look extremely complex.

A Free Computer Vision Lesson for Car Manufacturers or It is Time to Retire the Erlkönig [pdf]
Maximilian Weiherer, Bernhard Egger
Special Interest Group on Harry Quechua Bovik (SIGBOVIK), 2022

We blame car manufacturers by showing that camouflaged cars (a.k.a. Erlkönige) can be better 3D reconstructed than its uncovered counterparts (without disturbing patterns).

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