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Technische Universität München

Weihang Li

Picture of Weihang Li

M.Sc. Weihang Li

Technical University of Munich

Professional Services

Curriculum Vitae

Hi, I'm a PhD student with TUM CAMP & MCML supervised by Prof. Benjamin Busam and Prof. Nassir Navab. During my Master's study at TUM Robotics Cognition, Intelligence, I conducted research at CAMP, fortiss, Photogrammetry and Remote Sensing with Prof. Olaf Wysocki , HKUST-GZ with Prof. Haoang Li and CVG with Prof. Daniel Cremers.

Student Projects

If you are interested in the topics on Embodied AI, 3D Foundational Model and want to do a research project in Master Thesis / IDP / Guided Research / HiWi with us, feel free to reach out at any time with your CV and transcript :) We always welcome motivated students aiming for top-tier conferences and journals such as: CVPR/ICCV/NeurIPS/IJCV/TIP/ICRA and will offer supports with computation resources (A100/H100/RTX5090).

Open topics:
[1] Structured Semantics 3D Reconstruction (in collaboration with Cambridge)
Standard 3D reconstruction methods produce raw geometry: point clouds or meshes. The result is not a representation from which measurements can be directly extracted. 

This project targets a more useful output: given ground-level imagery of a building, produce a structured wireframe from which meaningful quantities can be read off directly - roof dimensions, slope angles, edge classifications. Such a representation has clear practical value, for example in estimating roof geometry and identifying optimal solar panel placement automatically.

Reference: arxiv.org/abs/2503.08208



[2] SLAM Foundation Models (in collaboration with Cambridge)

Our goal is to investigate how emerging 3D foundation models can be extended to support adaptive and continuous online scene mapping.
This research aims to bridge large-scale 3D representation learning with real-time SLAM and robotic perception in dynamic environments.
Some related references:
arxiv.org/abs/2512.25008
any-4d.github.io
arxiv.org/pdf/2601.09499

If this sounds interesting to you, we would be glad to arrange a follow-up discussion. We suggest the following format:

1. 10-minute presentation
    Your thoughts on the shared paper and the selected topic, including how your background might contribute.
2. 20-minute question and discussion
    Some questions and an open discussion to explore potential collaboration.

We accept remote collaboration, visiting Cambridge, or Master’s Thesis in our group.

Teaching

Awards

  • Honorable Mention Award in the S23DR Challenge at CVPR 2024.
  • Mentored Student Haoliang Huang win the 1st first place in the WLCOP Challenge at ICCV 2025

Publications

See Google Scholar

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