Student Assistant in the Context of (Semantic) 3D Surface Reconstruction Satellite Images

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30 000 employees work with an annual research budget of 2.9 billion euros.

The Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB) in Ettlingen supports research in different areas such as real-time image processing, pattern recognition, or performance optimization of electro-optical systems.

In the department Object Recognition we are working on the reconstruction of urban areas using multi-date satellite images. The corresponding three-dimensional models include not only texture information but also semantic labels. In order to manage the problem complexity, the task is decomposed into several, more controllable sub-problems. With the rapid advancement of computer vision there are frequently new possibilities available that allow to improve existing parts of our current processing pipeline.

Thus, we are offering several Student Assistant / Hiwi positions for improving specific sub-problems with current state-of-the-art techniques. Potential topics include (but are not necessarily limited to):

3D reconstruction / representation of satellite images with Neural Radiance Fields [1, 4], Structure from Motion [6], or similar.
Semantic segmentation of satellite images with SegFormers [5], Hierarchical Multi-Scale Attention [2], or similar.
Semantic segmentation of satellite images with missing labels with [3].
. . .

What you will do

The following outline represents a reference and might vary according to the selected topic:

Literature search including the determination of current state-of-the-art methods and corresponding datasets.
Adaptation of the selected methods to the domain of satellite images by addressing satellite-specific boundary conditions (if necessary).
Integration of the (adapted) method into our framework.
Quantitative evaluation of the developed method including a comparison with the baseline performance.
Extension of the approach using own ideas to improve the quality of the obtained results.

What you bring to the table

Enrolled in computer science or a similar field of study.
Solid programming skills (preferably in python).
High intrinsic motivation and interest to acquire knowledge of new methods reflecting the current state-of-the-art in computer vision.
Experience relevant for the chosen topic (e.g. lecture attendance, seminars or internships in computer vision such as deep learning, structure from motion or similar).

What you can expect

Transparent salary according to the general work agreement for employing assistant staff.
Support and guidance to publish your findings in a scientific paper (including conference attendance).
Monthly working hours that can be adjusted to your needs.
Due to the current pandemic situation we provide all required hardware components to work remotely. In order to receive and return the hardware, it is necessary to appear in person at the institute (Ettlingen, Baden-Württemberg) at the beginning / end of your contract!

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Interested? Apply online now and send us your application (including your CV and Transcript of Records).

We look forward to getting to know you!

If you have any questions please contact:

Dr.-Ing. Sebastian Bullinger

Tel.: +49 7243 992 197

References

[1] Dawa Derksen and Dario Izzo. Shadow neural radiance fields for multi-view satellite photogrammetry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021.
[2] Andrew Tao, Karan Sapra, and Bryan Catanzaro. Hierarchical multi-scale attention for semantic segmentation. CoRR, abs/2005.10821, 2020.
[3] Onur Tasar, Yuliya Tarabalka, and Pierre Alliez. Incremental learning for semantic segmentation of large-scale remote sensing data. CoRR, abs/1810.12448, 2018.

[4] Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504, 2021.
[5] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. arXiv preprint arXiv:2105.15203, 2021.
[6] Kai Zhang, Noah Snavely, and Jin Sun. Leveraging vision reconstruction pipelines for satellite imagery. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2019.

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB

www.iosb.fraunhofer.de

Requisition Number: 11583 Application Deadline:

Job Segment: Computer Science, Intern, Technology, Entry Level, Research

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