Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Self-supervised Monocular Depth Estimation with Geometric Regularities

We offer a master (or guided research) topic in the field of computer vision and robotics, which aims to reconstruct large-scale indoor environments efficiently.


Planar regions including floors, walls, ceilings and large surfaces of furniture are common elements in indoor scenes, and even main features of those environments. How to deal with those regions affects the scale we can reconstruct with limited computation. In this project, we make use of a consumer RGB-D camera and a CPU to reconstruct the main structure of the MI. building, which contains a few room scenes, corridors and stairs.


  • A strong background in SLAM/SfM. It would be better if you modified ORB-SLAM(V1,2,3) or other SLAM systems before.
  • Good C++ skill

If you are interested in any of these topics, please contact us via e-mail.

Yanyan Li

Federico Tombari

Title: Self-supervised Monocular Depth Estimation with Geometric Regularities
Director: Federico Tombari
Supervisor: Yanyan Li
Type: Master Thesis
Area: Machine Learning, Computer Vision
Status: open
Thesis (optional):  

Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 19 Sep 2020 - 21:06 - YanyanLi