Masterthesis (f/m/x) - Neural Horizon Mapping

Job Description

Req ID:

5118

Place of work:

Braunschweig

Starting date:

At the earliest opportunity

Career level:

Student research project and final thesis

Type of employment:

Part time, Full-time

Duration of contract:

up to 6 months

Remuneration: Remuneration is in accordance with the Collective Agreement for the Public Sector - Federal Government (TVöD-Bund).

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e. V.; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 12,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

What to expect

We develop CosmoScout VR, an open source software for rendering realistic images of large scale planetary surfaces in real-time. Applications include mission planning, generation of training data for pose estimation and immersive analysis of georeferenced datasets. One key criteria for realism is the presence of accurate shadows, however these are still challenging to produce for arbitrary scenes in real-time. We are looking for efficient ways of encoding and storing information required for computing large scale shadows for arbitrary lighting and viewing conditions.

Your tasks

Can neural representations be used to efficiently store and access occluder information of planetary terrains for computing shadows at runtime?

Lately, machine learning approaches have been used in various parts of the rendering pipeline to achieve high-quality visuals at a significantly lower memory footprint compared to non-ML pipelines. A key method is Neural texture compression: Here, a neural network is trained to reconstruct surface texture data from a compact latent representation. The core of this thesis is verifying the viability of applying this approach to so-called horizon maps, i.e. auxiliary textures used in computing self-shadows of planetary terrains.

Your profile

• Design and train a neural network for compressing and decompressing horizon maps

• Use the neural network to render shadows on a planetary surface in real-time

• Perform quantitative evaluations of performance and quality

We offer

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

We look forward to getting to know you!

If you have any questions about this position (Vacancy-ID 5118) please contact:

Jonathan Fritsch