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Handed in my dissertation thesis

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Today, I achieved a major milestone in my academic studies. After 3.5 years of hard work, I finally handed in my dissertation thesis at the Karlsruhe Institute of Technology.

Held my first lecture

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I held my first lecture together with my colleague Sebastian Krebs. We each did one of the 1.5 hour parts of the lecture “Digital Image Processing” at the University of Stuttgart. The lecture is normally held by Fabian Flohr.

Paper presented at AISTATS

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This week was the International Conference on Artificial Intelligence and Statistics (AISTATS). A total of 492 papers were presented (44 orals and 448 posters).

Gave an interview on publicly funded projects

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Mohsen Sefati and I recently gave an interview on how important publicly funded projects are for our research team at Mercedes-Benz AG. We reported about our experiences within the KI Delta Learning Project and explained how we can pursue innovative and challenging projects with the help of many partners.

publications

A Survey on Deep Domain Adaptation for LiDAR Perception

Published in IEEE Intelligent Vehicles Symposium (IV) Workshops, 2021

This paper presents a comprehensive review of recent progress in domain adaptation methods and formulates interesting research questions specifically targeted towards LiDAR perception.

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Semi-Local Convolutions for LiDAR Scan Processing

Published in Advances in Neural Information Processing Systems (NeurIPS) Workshops, 2021

This paper proposes semi local convolution (SLC), a convolution layer with reduced amount of weight-sharing along the vertical dimension.

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Point Cloud Generation with Continuous Conditioning

Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a continuous parameter.

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A Realism Metric for Generated LiDAR Point Clouds

Published in International Journal of Computer Vision (IJCV), 2022

This paper presents a novel metric to quantify the realism of LiDAR point clouds. We confirm that our metric provides an indication for the downstream segmentation performance.

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