Maolin Gao

Maolin Gao is a Researcher at the Computer Vision Group, TUM School of Computation, Information and Technology, and a PhD candidate at the Department of Informatics 9, Technical University of Munich (TUM). He is supervised by Prof. Daniel Cremers and was a visiting scholar at Stanford University's Geometric Computing Lab under Prof. Leonidas J. Guibas in Summer 2024.

Education:

• Bachelor in Physics from Tongji University (Shanghai), with exchange experience at TUM and RWTH Aachen
• Master's in Electrical Engineering and Information Technology from TUM, thesis on Blind Deconvolution supervised by Dr. Michael Hirsch, Prof. Philipp Hennig, and Prof. Bernhard Schölkopf at Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen

Research Focus: Maolin's work bridges geometry and learning to enhance human and machine understanding of 2D/3D data. His research spans 3D shape matching, non-rigid correspondence, geometric consistency, and deep learning applications in computer vision and graphics. He also explores photometric stereo, bundle adjustment, and scale-invariant matching methods.

Article Trends: His publications emphasize 3D shape analysis, including partial-to-partial matching, non-rigid deformations, and geometric consistency algorithms. Recent works like EchoMatch (CVPR 2025) and CoE (3DV 2025) highlight advancements in point cloud correspondences and cycle-consistency. Earlier papers address distributed optimization (3DV 2020) and isometric multi-shape matching (CVPR 2021).

Scientific Awards:

• Oral Presentation Award at 3DV 2020 for Distributed Photometric Bundle Adjustment

Labs & Collaborations: Affiliated with TUM CVAI Lab and Stanford Geometric Computing Lab. Collaborates with researchers like Prof. Florian Bernard and Dr. Michael Hirsch.