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Maolin Gao (高茂林)

I am a Ph.D. student at TUM CVAI Lab with Prof. Daniel Cremers, working on computer vision and optimisation.

At TUM, I've worked on Shape Analysis, Visual Simultaneous Localisation and Mapping (SLAM) and Photometric Stereo (PS).

Prior to my move to Munich, I was a computer vision engineer on ADAS Vision systems at Bosch Stuttgart. Prior to this, I obtained my Master's degree at TUM Department of Electrical Engineer and Information Technology, where I wrote my master thesis on Blind Deconvolution under the supervision of Dr. Michael Hirsch, Prof. Philipp Hennig and Prof. Bernhard Schölkopf in MPI-IS Tübingen. I obtained my Bachelor in Physics at Tongji University in Shanghai, during which I spent a year at TUM and a summer at RWTH Aachen.

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  • 2021/03: Our Paper on isometric multi-shape matching has been accepted as oral in CVPR 2021.
  • 2020/10: Our Paper on distributed photometric BA has been accepted as oral in 3DV in Fukuoka, Japan.
  • 2020/01: Moved back to CVAI Lab at TUM.
  • 2020/01: has been acquired by Kudan.
  • 2019/05: Our paper on general uncalibrated PS has been accepted in ICCV 2019 in Seoul, Korea.
  • 2018/11: Joined, an autonomous driving startup in Munich.
  • 2016/01: Joined Bosch ADAS vision department in Stuttgart.

Isometric Multi-Shape Matching
Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard
CVPR, 2021, Oral
paper / video / code(coming soon) / bibtex

An efficient and easy-to-implement isometric multi-shape matching algorithm utilising universe shape and functional maps.

Distributed Photometric Bundle Adjustment
Nikolaus Demmel, Maolin Gao, Emmanuel Laude, Tao Wu, Daniel Cremers
3DV, 2020, Oral
paper / project page / code / teaser / video / bibtex

An approximate consensus technique to optimise large-scale bundle adjustment in distribution.

Variational Uncalibrated Photometric Stereo Under General Lighting
Björn Häfner, Zhenzhang Ye, Maolin Gao, Tao Wu, Yvain Queau, Daniel Cremers
ICCV, 2019
paper / supplementary / code / bibtex

A variational approach to enable photometric stereo outside of laboratory conditions.

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