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

2024

  • L. Wiesmann, T. Läbe, L. Nunes, J. Behley, and C. Stachniss, “Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment,” IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 10, pp. 9103-9110, 2024. doi:10.1109/LRA.2024.3457385
    [BibTeX] [PDF]
    @article{wiesmann2024ral,
    author = {L. Wiesmann and T. L\"abe and L. Nunes and J. Behley and C. Stachniss},
    title = {{Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment}},
    journal = ral,
    year = {2024},
    volume = {9},
    number = {10},
    pages = {9103-9110},
    issn = {2377-3766},
    doi = {10.1109/LRA.2024.3457385},
    }

  • Y. Pan, X. Zhong, L. Wiesmann, T. Posewsky, J. Behley, and C. Stachniss, “PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency,” Ieee transactions on robotics (tro), vol. 40, pp. 4045-4064, 2024. doi:10.1109/TRO.2024.3422055
    [BibTeX] [PDF] [Code]
    @article{pan2024tro,
    author = {Y. Pan and X. Zhong and L. Wiesmann and T. Posewsky and J. Behley and C. Stachniss},
    title = {{PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency}},
    journal = tro,
    year = {2024},
    pages = {4045-4064},
    volume = {40},
    doi = {10.1109/TRO.2024.3422055},
    codeurl = {https://github.com/PRBonn/PIN_SLAM},
    }

  • D. Casado Herraez, L. Chang, M. Zeller, L. Wiesmann, J. Behley, M. Heidingsfeld, and C. Stachniss, “SPR: Single-Scan Radar Place Recognition,” IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 10, pp. 9079-9086, 2024.
    [BibTeX] [PDF]
    @article{casado-herraez2024ral,
    author = {Casado Herraez, D. and L. Chang and M. Zeller and L. Wiesmann and J. Behley and M. Heidingsfeld and C. Stachniss},
    title = {{SPR: Single-Scan Radar Place Recognition}},
    journal = ral,
    year = {2024},
    volume = {9},
    number = {10},
    pages = {9079-9086},
    }

  • Y. Wu, T. Guadagnino, L. Wiesmann, L. Klingbeil, C. Stachniss, and H. Kuhlmann, “LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{wu2024icra,
    author = {Y. Wu and T. Guadagnino and L. Wiesmann and L. Klingbeil and C. Stachniss and H. Kuhlmann},
    title = {{LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters}},
    booktitle = icra,
    year = 2024,
    codeurl = {https://github.com/YibinWu/LIO-EKF},
    videourl = {https://youtu.be/MoJTqEYl1ME},
    }

2023

  • R. Marcuzzi, L. Nunes, L. Wiesmann, E. Marks, J. Behley, and C. Stachniss, “Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 11, pp. 7487-7494, 2023. doi:10.1109/LRA.2023.3320020
    [BibTeX] [PDF] [Code] [Video]
    @article{marcuzzi2023ral-meem,
    author = {R. Marcuzzi and L. Nunes and L. Wiesmann and E. Marks and J. Behley and C. Stachniss},
    title = {{Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences}},
    journal = ral,
    year = {2023},
    volume = {8},
    number = {11},
    pages = {7487-7494},
    issn = {2377-3766},
    doi = {10.1109/LRA.2023.3320020},
    codeurl = {https://github.com/PRBonn/Mask4D},
    videourl = {https://youtu.be/4WqK_gZlpfA},
    }

  • I. Vizzo, B. Mersch, L. Nunes, L. Wiesmann, T. Guadagnino, and C. Stachniss, “Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition,” in Proc. of the intl. conf. on intelligent transportation systems workshops, 2023.
    [BibTeX] [PDF] [Code]
    @inproceedings{vizzo2023itcsws,
    author = {I. Vizzo and B. Mersch and L. Nunes and L. Wiesmann and T. Guadagnino and C. Stachniss},
    title = {{Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition}},
    booktitle = {Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops},
    year = 2023,
    codeurl = {https://github.com/ipb-car/meta-workspace},
    note = {accepted}
    }

  • L. Wiesmann, T. Guadagnino, I. Vizzo, N. Zimmerman, Y. Pan, H. Kuang, J. Behley, and C. Stachniss, “LocNDF: Neural Distance Field Mapping for Robot Localization,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, p. 4999–5006, 2023. doi:10.1109/LRA.2023.3291274
    [BibTeX] [PDF] [Code] [Video]
    @article{wiesmann2023ral-icra,
    author = {L. Wiesmann and T. Guadagnino and I. Vizzo and N. Zimmerman and Y. Pan and H. Kuang and J. Behley and C. Stachniss},
    title = {{LocNDF: Neural Distance Field Mapping for Robot Localization}},
    journal = ral,
    volume = {8},
    number = {8},
    pages = {4999--5006},
    year = 2023,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2023ral-icra.pdf},
    issn = {2377-3766},
    doi = {10.1109/LRA.2023.3291274},
    codeurl = {https://github.com/PRBonn/LocNDF},
    videourl = {https://youtu.be/-0idH21BpMI},
    }

  • E. Marks, M. Sodano, F. Magistri, L. Wiesmann, D. Desai, R. Marcuzzi, J. Behley, and C. Stachniss, “High Precision Leaf Instance Segmentation in Point Clouds Obtained Under Real Field Conditions,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, pp. 4791-4798, 2023. doi:10.1109/LRA.2023.3288383
    [BibTeX] [PDF] [Code] [Video]
    @article{marks2023ral,
    author = {E. Marks and M. Sodano and F. Magistri and L. Wiesmann and D. Desai and R. Marcuzzi and J. Behley and C. Stachniss},
    title = {{High Precision Leaf Instance Segmentation in Point Clouds Obtained Under Real Field Conditions}},
    journal = ral,
    pages = {4791-4798},
    volume = {8},
    number = {8},
    issn = {2377-3766},
    year = {2023},
    doi = {10.1109/LRA.2023.3288383},
    codeurl = {https://github.com/PRBonn/plant_pcd_segmenter},
    videourl = {https://youtu.be/dvA1SvQ4iEY}
    }

  • L. Nunes, L. Wiesmann, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, “Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2023.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{nunes2023cvpr,
    author = {L. Nunes and L. Wiesmann and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
    title = {{Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving}},
    booktitle = cvpr,
    year = 2023,
    codeurl = {https://github.com/PRBonn/TARL},
    videourl = {https://youtu.be/0CtDbwRYLeo},
    }

  • I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, “KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 2, pp. 1-8, 2023. doi:10.1109/LRA.2023.3236571
    [BibTeX] [PDF] [Code] [Video]
    @article{vizzo2023ral,
    author = {Vizzo, Ignacio and Guadagnino, Tiziano and Mersch, Benedikt and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},
    title = {{KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way}},
    journal = ral,
    pages = {1-8},
    doi = {10.1109/LRA.2023.3236571},
    volume = {8},
    number = {2},
    year = {2023},
    codeurl = {https://github.com/PRBonn/kiss-icp},
    videourl = {https://youtu.be/h71aGiD-uxU}
    }

  • R. Marcuzzi, L. Nunes, L. Wiesmann, J. Behley, and C. Stachniss, “Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 2, p. 1141–1148, 2023. doi:10.1109/LRA.2023.3236568
    [BibTeX] [PDF] [Code] [Video]
    @article{marcuzzi2023ral,
    author = {R. Marcuzzi and L. Nunes and L. Wiesmann and J. Behley and C. Stachniss},
    title = {{Mask-Based Panoptic LiDAR Segmentation for Autonomous Driving}},
    journal = ral,
    volume = {8},
    number = {2},
    pages = {1141--1148},
    year = 2023,
    doi = {10.1109/LRA.2023.3236568},
    videourl = {https://youtu.be/I8G9VKpZux8},
    codeurl = {https://github.com/PRBonn/MaskPLS},
    }

  • L. Wiesmann, L. Nunes, J. Behley, and C. Stachniss, “KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 2, pp. 592-599, 2023. doi:10.1109/LRA.2022.3228174
    [BibTeX] [PDF] [Code] [Video]
    @article{wiesmann2023ral,
    author = {L. Wiesmann and L. Nunes and J. Behley and C. Stachniss},
    title = {{KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition}},
    journal = ral,
    volume = {8},
    number = {2},
    pages = {592-599},
    year = 2023,
    issn = {2377-3766},
    doi = {10.1109/LRA.2022.3228174},
    codeurl = {https://github.com/PRBonn/kppr},
    videourl = {https://youtu.be/bICz1sqd8Xs}
    }

  • M. Arora, L. Wiesmann, X. Chen, and C. Stachniss, “Static Map Generation from 3D LiDAR Point Clouds Exploiting Ground Segmentation,” Robotics and autonomous systems, vol. 159, p. 104287, 2023. doi:https://doi.org/10.1016/j.robot.2022.104287
    [BibTeX] [PDF] [Code]
    @article{arora2023jras,
    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,
    volume = {159},
    pages = {104287},
    year = {2023},
    issn = {0921-8890},
    doi = {https://doi.org/10.1016/j.robot.2022.104287},
    codeurl = {https://github.com/PRBonn/dynamic-point-removal},
    }

2022

  • 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 Intl. 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. 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 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 = 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 = {https://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 = {https://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 = {https://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.},
    }