Maolin Gao (高茂林)

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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, graphics and optimisation. I was a visiting scholar at the Geometric Computing Lab at Stanford University headed by Prof. Leonidas J. Guibas in the summer 2024.

My current research centers on the intersection of geometry and learning, aiming to enhance both human and machine understanding of 3D data.

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.

News

2024/11: Our paper on embedding learning for point clouds got accepted in 3DV 2025 in Singapore.
2024/08: One paper on neural surface reconstruction will apear in GCPR 2024 in Munich, Germany.
2024/07: I will visit the Geometric Computing Lab at Stanford and working with Prof. Leonidas J. Guibas for this summer.
2024/03: Two papers have been accepted in CVPR 2024 in Seattle, USA.
1. Finsler-Laplace-Beltrami Operators with Application to Shape Analysis.
2. Partial-to-Partial Shape Matching with Geometric Consistency.
2023/10: Our paper on consistent partial shape matching has been accepted in 3DV 2024 in Davos, Switzerland.
2023/08: Our paper on global shape matching has been accepted in ICCV 2023 in Paris, France.
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: Artisense.ai 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 Artisense.ai, an autonomous driving startup in Munich.
2016/01: Joined Bosch ADAS vision department in Stuttgart.

Projects

	Finsler-Laplace-Beltrami Operators with Application to Shape Analysis Simon Weber, Tom Dagès, Maolin Gao, Daniel Cremers CVPR, 2024 paper / code / video / bibtex A solution of heat equation on the Finsler manifold which induces natural anisotropy.
	Partial-to-Partial Shape Matching with Geometric Consistency Viktoria Ehm, Maolin Gao, Paul Roetzer, Marvin Eisenberger, Florian Bernard, Daniel Cremers CVPR, 2024 paper / webpage / bibtex Partial-to-partial shape matching approaches guided by geometric consistency.
	Geometrically Consistent Partial Shape Matching Viktoria Ehm, Paul Roetzer, Marvin Eisenberger, Maolin Gao, Florian Bernard, Daniel Cremers 3DV, 2024 paper / code / bibtex Partial-to-full shape matching approaches encourages geometric consistency.
	SIGMA: Scale-Invariant Global Sparse Shape Matching Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Moeller, Daniel Cremers, Florian Bernard ICCV, 2023 paper(25MB) / paper(600KB) / suppmat(9MB) / code / bibtex A shape construction and matching method which can be solved to global optimality.
	Isometric Multi-Shape Matching Maolin Gao, Zorah Lähner, Johan Thunberg, Daniel Cremers, Florian Bernard CVPR, 2021, Oral paper / video / code / 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.