Wednesday, March 01, 2023

Deep Learning driven Domain Adaptation of Point Clouds

Topic and Goal of the Thesis

The training of neural networks for object detection requires a large amount of data, whose manual annotation is very time-consuming. An alternative is the use of simulation data, but these rarely manage to sufficiently represent real conditions. In this thesis, approaches based on a Generative Adversarial Network (GAN) will be investigated to improve the fidelity of automatically generated simulation data.

Working Points

  • Literature review on the use of GANs for domain adaptation of point clouds
  • Extension and optimization of an existing network architecture
  • Training of the GAN with the help of an existing framework
  • Evaluation of proposed techniques


  • Good English or German language skills
  • Experience with Python
  • Experience with Machine Learning is an advantage (not a must)
  • Reliability, commitment and enjoyment of working independently

Note: Please attach brief resume and grade summary.


Amarin Vincent Klöker M.Sc.
+49 241 80 25589

Type of work

Bachelorarbeit, Masterarbeit


Earliest possible date

Prior knowledge

Programming skills


Deutsch, Englisch

Research area

Fahrzeugintelligenz & Automatisiertes Fahren


Institute for Automotive Engineering
RWTH Aachen University
Steinbachstraße 7
52074 Aachen · Germany
+49 241 80 25600

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