Samuel Brucker

Samuel Brucker

My research focuses on 3D perception for autonomous driving, particularly in the context of highway driving and challenging environmental conditions. I work on long-range accurate depth prediction for highway scenarios using multi-view geometry, aiming to improve 3D understanding at the distances required for high-speed autonomy. I investigate multi-modal sensor fusion with RGB, LiDAR, and novel sensors to enhance perception under adverse weather, nighttime, and limited-visibility conditions. My broader goal is to enable autonomous vehicles to achieve reliable and human-like spatial understanding in complex real-world environments.

Contact

office
Room 1351 at ZIB
e-mail
languages
German and English

Curriculum vitae

since 2026
Researcher at ZIB

Publications and preprints

Preprints

  1. Ghilotti, F., Palladin, E., Brucker, S., Sigal, A., Bijelic, M., and Heide, F. (2026). TruckDrive: Long-Range Autonomous Highway Driving Dataset. [arXiv]
    [BibTeX]
    @misc{2026_GhilottiEtAl_Truckdrivedataset_2603-02413,
  archiveprefix = {arXiv},
  eprint = {2603.02413},
  arxiv = {arXiv:2603.02413},
  primaryclass = {cs.CV},
  author = {Ghilotti, Filippo and Palladin, Edoardo and Brucker, Samuel and Sigal, Adam and Bijelic, Mario and Heide, Felix},
  title = {TruckDrive: Long-Range Autonomous Highway Driving Dataset},
  date = {2026-03-02}
}
  2. Ghilotti, F., Brucker, S., Saidy, N., Matteucci, M., Bijelic, M., and Heide, F. (2026). UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition. [arXiv]
    [BibTeX]
    @misc{2026_GhilottiEtAl_Unilipspseudolabeling_2601-05105,
  archiveprefix = {arXiv},
  eprint = {2601.05105},
  arxiv = {arXiv:2601.05105},
  primaryclass = {cs.CV},
  author = {Ghilotti, Filippo and Brucker, Samuel and Saidy, Nahku and Matteucci, Matteo and Bijelic, Mario and Heide, Felix},
  title = {UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition},
  date = {2026-01-08}
}

Conference proceedings

  1. Palladin, E., Brucker, S., Ghilotti, F., Narayanan, P., Bijelic, M., and Heide, F. (2025). Self-Supervised Sparse Sensor Fusion for Long Range Perception. Proceedings of the IEEE/CVF International Conference on Computer Vision, 27498–27509. DOI: 10.1109/iccv51701.2025.02553 [arXiv]
    [BibTeX]
    @inproceedings{2025_PalladinEtAl_Selfsupervisedfusion_2508-13995,
  year = {2025},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages = {27498--27509},
  doi = {10.1109/iccv51701.2025.02553},
  archiveprefix = {arXiv},
  eprint = {2508.13995},
  arxiv = {arXiv:2508.13995},
  primaryclass = {cs.CV},
  author = {Palladin, Edoardo and Brucker, Samuel and Ghilotti, Filippo and Narayanan, Praveen and Bijelic, Mario and Heide, Felix},
  title = {Self-Supervised Sparse Sensor Fusion for Long Range Perception}
}
  2. Brucker, S., Walz, S., Bijelic, M., and Heide, F. (2024). Cross-spectral Gated-RGB Stereo Depth Estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 21654–21665. DOI: 10.1109/cvpr52733.2024.02046 [arXiv]
    [BibTeX]
    @inproceedings{2024_BruckerWalzBijelicHeide_Gatedrgbstereo_2405-12759,
  year = {2024},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages = {21654--21665},
  doi = {10.1109/cvpr52733.2024.02046},
  archiveprefix = {arXiv},
  eprint = {2405.12759},
  arxiv = {arXiv:2405.12759},
  primaryclass = {cs.CV},
  author = {Brucker, Samuel and Walz, Stefanie and Bijelic, Mario and Heide, Felix},
  title = {Cross-spectral Gated-RGB Stereo Depth Estimation}
}

Projects

GENAD
Generative AI for Scalable Autonomous Driving Systems

The project aims at shaping the next generation of highway autonomy for heavy trucks by combining long-range multimodal data, controllable generative models, a closed-loop virtual gym, and vision-language model (VLM) embedded reasoning. Rather than treating autonomy as a pure perception or planning problem, the project aims to advance generative simulation, reinforcement learning and chain-of-thought reasoning and language-guided representation learning as a unified research agenda.

GENAD
Mar 2026 to Mar 2029
5