About me

Hi there! 👋 I’m Stefan, a ML Engineer working on autonomous driving at Applied Intuition. Prior to joining Applied, I worked at Mercedes-Benz on perception for driver assistance systems.

I have obtained my PhD as part of the Autonomous Vision Group at the University of Tübingen, supervised by Andreas Geiger. The research during my PhD focused on motion-based self-supervised learning for 3D object detection, using LiDAR. You can find my thesis here.

I strongly believe in the Bitter Lesson: In the long run, methods that can make better use of data and compute tend to outperform all the clever tricks we come up with as computer vision researchers. That said, I think motion-based self-supervised learning in 3D is a powerful shortcut for models to obtain a deeper world understanding: It scales with data and compute, while taking advantage of how things actually move and exist in 3D space without requiring any human annotations.

Please check out my related publications below:

Publications

• PhD Thesis: Learning 3D LiDAR Object Detection without Human Annotations
  • Stefan Baur 2025, Faculty of Science, University of Tubingen
• LISO: Lidar-only Self-Supervised 3D Object Detection
  • Stefan Baur, Frank Moosmann and Andreas Geiger
  • 2024 European Conference on Computer Vision (ECCV)
  • Paper
• SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
  • Stefan Baur^*, David Emmerichs^*, Frank Moosmann, Peter Pinggera, Bjorn Ommer and Andreas Geiger (^*: equal conribution)
  • 2021 International Conference on Computer Vision (ICCV), Oral
  • Paper
• Quantifying point cloud realism through adversarially learned latent representations
  • Larissa T. Triess, David Peter, Stefan Baur and J. Marius Zöllner
  • 2021 Proc. of the German Conference on Pattern Recognition (GCPR)
• PillarFlowNet: A Real-time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation
  • Fabian Duffhaus; Stefan Baur
  • 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• Real-time 3D LiDAR Flow for Autonomous Vehicles (Oral)
  • Stefan A. Baur; Frank Moosmann; Sascha Wirges; Christoph B. Rist
  • 2019 IEEE Intelligent Vehicles Symposium (IV)

Misc

• Co-chair of the session “Range Sensing and Deep Learning” @ 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Software Projects

• RBRT: A lightweight Ray Tracer that I wrote to dive into raytracing and Rust.
  • fast: supports Intel AVX & SSE SIMD instructions
  • meshes can be loaded from .obj files
• Sudoku Solver:
  • uses constraint propagation and backtracking
  • Rust compiled to WebAssembly with React Frontend
  • try it out (runs completely in the browser)
• Snake: A terminal based snake game
  • no window manager or GUI required
  • cross platform: Latest Release (Windows & Linux).