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