Louis Wiesmann

Louis Wiesmann

Ph.D. Student
Contact:
Email: louis.wiesmann@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 29 06
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.006
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Short CV

Louis Wiesmann is a PhD student at the Photogrammetry Lab at the University of Bonn since November 2019. He received his master’s degree at the Institute of Geodesy and Geoinformation in 2019.

Research Interests

  • SLAM
  • Computer Vision
  • Machine Learning

Awards

  • Turbo-Preis 2019 of the DVW

Publications

2022

  • M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, “Static Map Generation from 3D LiDAR Point Clouds Exploiting Ground Segmentation,” Robotics and autonomous systems, 2022.
    [BibTeX] [Code]
    @article{arora2022jras,
    author = {M. Arora and L. Wiesmann and X. Chen and C. Stachniss},
    title = {{Static Map Generation from 3D LiDAR Point Clouds Exploiting Ground Segmentation}},
    journal = jras,
    year = {2022},
    codeurl = {https://github.com/PRBonn/dynamic-point-removal},
    note = {Accepted for publication}
    }

  • N. Zimmerman, L. Wiesmann, T. Guadagnino, T. Läbe, J. Behley, and C. Stachniss, “Robust Onboard Localization in Changing Environments Exploiting Text Spotting,” in Proc. of the ieee/rsj int. conf. on intelligent robots and systems (iros), 2022.
    [BibTeX] [PDF] [Code]
    @inproceedings{zimmerman2022iros,
    title = {{Robust Onboard Localization in Changing Environments Exploiting Text Spotting}},
    author = {N. Zimmerman and L. Wiesmann and T. Guadagnino and T. Läbe and J. Behley and C. Stachniss},
    booktitle = iros,
    year = {2022},
    codeurl = {https://github.com/PRBonn/tmcl},
    }

  • I. Vizzo, B. Mersch, R. Marcuzzi, L. Wiesmann, J. and Behley, and C. Stachniss, “Make it dense: self-supervised geometric scan completion of sparse 3d lidar scans in large outdoor environments,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, pp. 8534-8541, 2022. doi:10.1109/LRA.2022.3187255
    [BibTeX] [PDF] [Code] [Video]
    @article{vizzo2022ral,
    author = {I. Vizzo and B. Mersch and R. Marcuzzi and L. Wiesmann and and J. Behley and C. Stachniss},
    title = {Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments},
    journal = ral,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2022ral-iros.pdf},
    codeurl = {https://github.com/PRBonn/make_it_dense},
    year = {2022},
    volume = {7},
    number = {3},
    pages = {8534-8541},
    doi = {10.1109/LRA.2022.3187255},
    videourl = {https://youtu.be/NVjURcArHn8},
    }

  • L. Wiesmann, T. Guadagnino, I. Vizzo, G. Grisetti, J. Behley, and C. Stachniss, “DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 3, pp. 6327-6334, 2022. doi:10.1109/LRA.2022.3171068
    [BibTeX] [PDF] [Code] [Video]
    @article{wiesmann2022ral-iros,
    author = {L. Wiesmann and T. Guadagnino and I. Vizzo and G. Grisetti and J. Behley and C. Stachniss},
    title = {{DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments}},
    journal = ral,
    year = 2022,
    volume = 7,
    number = 3,
    pages = {6327-6334},
    issn = {2377-3766},
    doi = {10.1109/LRA.2022.3171068},
    codeurl = {https://github.com/PRBonn/DCPCR},
    videourl = {https://youtu.be/RqLr2RTGy1s},
    }

  • L. Wiesmann, R. Marcuzzi, C. Stachniss, and J. Behley, “Retriever: Point Cloud Retrieval in Compressed 3D Maps,” in Proc.~of the ieee intl.~conf.~on robotics & automation (icra), 2022.
    [BibTeX] [PDF]
    @inproceedings{wiesmann2022icra,
    author = {L. Wiesmann and R. Marcuzzi and C. Stachniss and J. Behley},
    title = {{Retriever: Point Cloud Retrieval in Compressed 3D Maps}},
    booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
    year = 2022,
    }

  • R. Marcuzzi, L. Nunes, L. Wiesmann, I. Vizzo, J. Behley, and C. Stachniss, “Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans,” Ieee robotics and automation letters (ra-l), vol. 7, iss. 2, pp. 1550-1557, 2022. doi:10.1109/LRA.2022.3140439
    [BibTeX] [PDF]
    @article{marcuzzi2022ral,
    author = {R. Marcuzzi and L. Nunes and L. Wiesmann and I. Vizzo and J. Behley and C. Stachniss},
    title = {{Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans}},
    journal = ral,
    year = 2022,
    doi = {10.1109/LRA.2022.3140439},
    issn = {2377-3766},
    volume = 7,
    number = 2,
    pages = {1550-1557},
    }

2021

  • M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, “Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation,” in Proc. of the european conf. on mobile robots (ecmr), 2021.
    [BibTeX] [PDF] [Code]
    @InProceedings{arora2021ecmr,
    author = {M. Arora and L. Wiesmann and X. Chen and C. Stachniss},
    title = {{Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation}},
    booktitle = ecmr,
    codeurl = {https://github.com/humbletechy/Dynamic-Point-Removal},
    year = {2021},
    }

  • X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, “Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data,” Ieee robotics and automation letters (ra-l), vol. 6, pp. 6529-6536, 2021. doi:10.1109/LRA.2021.3093567
    [BibTeX] [PDF] [Code] [Video]
    @article{chen2021ral,
    title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},
    author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},
    year={2021},
    volume=6,
    issue=4,
    pages={6529-6536},
    journal=ral,
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021ral-iros.pdf},
    codeurl = {https://github.com/PRBonn/LiDAR-MOS},
    videourl = {https://youtu.be/NHvsYhk4dhw},
    doi = {10.1109/LRA.2021.3093567},
    issn = {2377-3766},
    }

  • L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep Compression for Dense Point Cloud Maps,” Ieee robotics and automation letters (ra-l), vol. 6, pp. 2060-2067, 2021. doi:10.1109/LRA.2021.3059633
    [BibTeX] [PDF] [Code] [Video]
    @article{wiesmann2021ral,
    author = {L. Wiesmann and A. Milioto and X. Chen and C. Stachniss and J. Behley},
    title = {{Deep Compression for Dense Point Cloud Maps}},
    journal = ral,
    volume = 6,
    issue = 2,
    pages = {2060-2067},
    doi = {10.1109/LRA.2021.3059633},
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/wiesmann2021ral.pdf},
    codeurl = {https://github.com/PRBonn/deep-point-map-compression},
    videourl = {https://youtu.be/fLl9lTlZrI0}
    }

2020

  • C. Stachniss, I. Vizzo, L. Wiesmann, and N. Berning, How To Setup and Run a 100\% Digital Conf.: DIGICROP 2020, 2020.
    [BibTeX] [PDF]

    The purpose of this record is to document the setup and execution of DIGICROP 2020 and to simplify conducting future online events of that kind. DIGICROP 2020 was a 100\% virtual conference run via Zoom with around 900 registered people in November 2020. It consisted of video presentations available via our website and a single-day live event for Q&A. We had around 450 people attending the Q&A session overall, most of the time 200-250 people have been online at the same time. This document is a collection of notes, instructions, and todo lists. It is not a polished manual, however, we believe these notes will be useful for other conference organizers and for us in the future.

    @misc{stachniss2020digitalconf,
    author = {C. Stachniss and I. Vizzo and L. Wiesmann and N. Berning},
    title = {{How To Setup and Run a 100\% Digital Conf.: DIGICROP 2020}},
    year = {2020},
    url = {http://www.ipb.uni-bonn.de/pdfs/stachniss2020digitalconf.pdf},
    abstract = {The purpose of this record is to document the setup and execution of DIGICROP 2020 and to simplify conducting future online events of that kind. DIGICROP 2020 was a 100\% virtual conference run via Zoom with around 900 registered people in November 2020. It consisted of video presentations available via our website and a single-day live event for Q&A. We had around 450 people attending the Q&A session overall, most of the time 200-250 people have been online at the same time. This document is a collection of notes, instructions, and todo lists. It is not a polished manual, however, we believe these notes will be useful for other conference organizers and for us in the future.},
    }