Master Thesis: Mesh generation with machine learning

Extension of Shrinkingtubemesh-generation (as illustrated below) with machine learning. The current version was written in matlab and is only suitable for cylinder-like structures, for example vessels. The program should be adjusted to fit a wider variation of shapes.
Subtasks: The task can be solved in two ways: using classical machine learning or with deep learning.

Option 1: Maschine Learning

  • including development of a range of suitable startshapes, definition of point cloud features for machine learning, generation of a Testdatabase, usage of Machine Learning to predict a suitable startshape and shrinkingtubemesh-algorithm parameter for a given pointcloud .

Option 2: Deep Learning:

  • Generate startshapes (simple, roughly the pointcloud describing meshes) using deep learning (for example using a pointcloud to mesh approach like AtlasNet);
  • use these startshapes for the shrinkingtubemesh generation and compare to other mesh generation approaches .

Requirements: Knowledge of Python (Pytorch) and Matlab; Experience in Machine Learning/Deep Learning

We expect high-qualified students interested in this project (hiwi job / student assistant or team projects, bachelor or master thesis). Please send your application!