Simon Doll

Hey there! I am Simon, a PhD student with the Scene Understanding Group at Mercedes-Benz R&D, as well as the University of Tübingen where I am advised by Prof. Hendrik P.A. Lensch.

My research interests lie in machine learning, especially at the intersection between computer vision and robotics. In particular, I am interested in incorporating physical knowledge to learned representations as well as finding robust and scalable representations for multi-modal perception.

Additionally, I founded a startup with my twin brother in healthcare where we focused on machine learning methods for vision centric applications in dermatology.

Besides research I am a passionate musician, love surfing and I am always in for a good cup of Coffee, Espresso - or anything else with caffeine.

selected publications

SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images With Global Cross-Sensor Attention

Simon Doll, Richard Schulz, Lukas Schneider, and 3 more authors

In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIX, 2022

Bib PDF Code Project Page Video

@inproceedings{doll2022spatialdetr,
  title = {SpatialDETR: Robust Scalable Transformer-Based 3D Object Detection From Multi-view Camera Images With Global Cross-Sensor Attention},
  author = {Doll, Simon and Schulz, Richard and Schneider, Lukas and Benzin, Viviane and Enzweiler, Markus and Lensch, Hendrik PA},
  booktitle = {Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXIX},
  pages = {230--245},
  year = {2022},
  organization = {Springer},
}

S.T.A.R.-Track - Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations

Simon Doll, Hanselmann Niklas, Lukas Schneider, and 3 more authors

2023

Bib PDF Project Page Video

@misc{doll2022startrack,
  title = {S.T.A.R.-Track - Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations},
  author = {Doll, Simon and Niklas, Hanselmann and Schneider, Lukas and Schulz, Richard and Enzweiler, Markus and Lensch, Hendrik PA},
  year = {2023},
  eprint = {2306.17602},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
}