Federico Magistri

PhD Student
Contact:
Email: federico.magistri@nulluni-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

Google Scholar | LinkedIn

Research Interests

  • Computer Vision
  • Machine Learning
  • Multi-Robot Systems
  • Agricultural Robotics

Short CV

Federico Magistri is a Ph.D. student at the Photogrammetry Lab at the University of Bonn since November 2019. He has been a visiting student at the Centre for Robotics of the Queensland University of Technology in fall 2023. He received his M.Sc. in Artificial Intelligence and Robotics from “La Sapienza” University of Rome with a thesis on Swarm Robotics for Precision Agriculture in collaboration with the National Research Council of Italy and the Wageningen University and Research. During his master, he spent one semester at the University of Freiburg as an Erasmus student.

Awards

  • Finalist of the Best Paper Award in Service Robotics for the paper “Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots” at the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024. [PDF]
  • Winner of the Outstanding Automation Paper Award for the paper “Target-Aware Implicit Mapping for Agricultural Crop Inspection” at the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023. [PDF]
  • Finalist of the Best Paper Award on Agri-Robotics for the paper “Constrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB-D Frames” at the Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2022. [PDF]
  • Winner of the Outstanding Automation Paper Award for the paper “Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching” at the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2022.  [PDF]

Projects

  • PhenoRob – Robotics and Phenotyping for Sustainable Crop Production (DFG Cluster of Excellence)
  • SAGA – Swarm Robotics for Precision Agriculture (founded by the ECHORD++ project)

Teaching

  • Master Project:
    • Informative Path Planning for UAV-based Crop/Weed Segmentation, 2020
    • Visual-Inertial Odometry in Orchard Environments, 2021
    • Visual-Haptic Robust Navigation for Under Canopy Exploration, 2022
    • Autonomous Drone Mapping in Orchard Environments, 2024
  • 3D Coordinate Systems – from Winter Semester 2020 to Summer Semester 2023

Publications

2024

  • M. Sodano, F. Magistri, L. Nunes, J. Behley, and C. Stachniss, “Open-World Semantic Segmentation Including Class Similarity,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
    [BibTeX] [PDF] 
    @inproceedings{sodano2024cvpr,
author = {M. Sodano and F. Magistri and L. Nunes and J. Behley and C. Stachniss},
title = {{Open-World Semantic Segmentation Including Class Similarity}},
booktitle = cvpr,
year = 2024,
}
  • F. Magistri, R. Marcuzzi, E. A. Marks, M. Sodano, J. Behley, and C. Stachniss, “Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024.
    [BibTeX] [PDF] [Video] 
    @inproceedings{magistri2024icra,
author = {F. Magistri and R. Marcuzzi and E.A. Marks and M. Sodano and J. Behley and C. Stachniss},
title = {{Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots}},
booktitle = icra,
year = 2024,
videourl = {https://youtu.be/U1xxnUGrVL4},
}
  • J. Rückin, F. Magistri, C. Stachniss, and M. Popović, “Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning,” IEEE Robotics and Automation Letters (RA-L), vol. 9, iss. 3, pp. 2662-2669, 2024. doi:10.1109/LRA.2024.3359970
    [BibTeX] 
    @article{rueckin2024ral,
author = {J. R\"uckin and F. Magistri and C. Stachniss and M. Popovi\'c},
title = {{Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning}},
journal = ral,
year = {2024},
volume = {9},
number = {3},
pages = {2662-2669},
issn = {2377-3766},
doi = {10.1109/LRA.2024.3359970},
}

2023

  • G. Roggiolani, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Unsupervised Pre-Training for 3D Leaf Instance Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 8, pp. 7448-7455, 2023. doi:10.1109/LRA.2023.3320018
    [BibTeX] [PDF] [Code] [Video] 
    @article{roggiolani2023ral,
author = {G. Roggiolani and F. Magistri and T. Guadagnino and J. Behley and C. Stachniss},
title = {{Unsupervised Pre-Training for 3D Leaf Instance Segmentation}},
journal = ral,
year = {2023},
volume = {8},
issue = {11},
codeurl = {https://github.com/PRBonn/Unsupervised-Pre-Training-for-3D-Leaf-Instance-Segmentation},
pages = {7448-7455},
doi = {10.1109/LRA.2023.3320018},
issn = {2377-3766},
videourl = {https://youtu.be/PbYVPPwVeKg},
}
  • J. Rückin, F. Magistri, C. Stachniss, and M. Popovic, “An informative path planning framework for active learning in uav-based semantic mapping,” Ieee transactions on robotics (tro), 2023.
    [BibTeX] [PDF] [Code] 
    @article{rueckin2023tro,
author = {J. R\"{u}ckin and F. Magistri and C. Stachniss and M. Popovic},
title = {An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping},
journal = tro,
year = {2023},
codeurl = {https://github.com/dmar-bonn/ipp-al-framework}
}
  • F. Magistri, J. Weyler, D. Gogoll, P. Lottes, J. Behley, N. Petrinic, and C. Stachniss, “From one field to another – unsupervised domain adaptation for semantic segmentation in agricultural robotics,” Computers and electronics in agriculture, vol. 212, p. 108114, 2023. doi:https://doi.org/10.1016/j.compag.2023.108114
    [BibTeX] [PDF] 
    @article{magistri2023cea,
author = {F. Magistri and J. Weyler and D. Gogoll and P. Lottes and J. Behley and N. Petrinic and C. Stachniss},
title = {From one Field to Another – Unsupervised Domain Adaptation for Semantic Segmentation in Agricultural Robotics},
journal = cea,
year = {2023},
volume = {212},
pages = {108114},
doi = {https://doi.org/10.1016/j.compag.2023.108114},
}
  • Y. Pan, F. Magistri, T. Läbe, E. Marks, C. Smitt, C. S. McCool, J. Behley, and C. Stachniss, “Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{pan2023iros,
author = {Y. Pan and F. Magistri and T. L\"abe and E. Marks and C. Smitt and C.S. McCool and J. Behley and C. Stachniss},
title = {{Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots}},
booktitle = iros,
year = 2023,
codeurl = {https://github.com/PRBonn/HortiMapping},
videourl = {https://youtu.be/fSyHBhskjqA}
}
  • J. Weyler, F. Magistri, E. Marks, Y. L. Chong, M. Sodano, G. Roggiolani, N. Chebrolu, C. Stachniss, and J. Behley, “PhenoBench –- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,” Arxiv preprint, vol. arXiv:2306.04557, 2023.
    [BibTeX] [PDF] [Code] 
    @article{weyler2023arxiv,
author = {Jan Weyler and Federico Magistri and Elias Marks and Yue Linn Chong and Matteo Sodano
and Gianmarco Roggiolani and Nived Chebrolu and Cyrill Stachniss and Jens Behley},
title = {{PhenoBench --- A Large Dataset and Benchmarks for Semantic Image Interpretation
in the Agricultural Domain}},
journal = {arXiv preprint},
volume = {arXiv:2306.04557},
year = {2023},
codeurl = {https://github.com/PRBonn/phenobench}
}
  • 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}
}
  • J. Weyler, T. Läbe, F. Magistri, J. Behley, and C. Stachniss, “Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 6, pp. 3310-3317, 2023. doi:10.1109/LRA.2023.3262417
    [BibTeX] [PDF] 
    @article{weyler2023ral,
author = {J. Weyler and T. L\"abe and F. Magistri and J. Behley and C. Stachniss},
title = {{Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots}},
journal = ral,
pages = {3310-3317},
volume = 8,
number = 6,
issn = {2377-3766},
year = {2023},
doi = {10.1109/LRA.2023.3262417}
}
  • M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Robust Double-Encoder Network for RGB-D Panoptic Segmentation,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{sodano2023icra,
author = {Matteo Sodano and Federico Magistri and Tiziano Guadagnino and Jens Behley and Cyrill Stachniss},
title = {{Robust Double-Encoder Network for RGB-D Panoptic Segmentation}},
booktitle = icra,
year = 2023,
codeurl = {https://github.com/PRBonn/PS-res-excite},
videourl = {https://youtu.be/r1pabV3sQYk}
}
  • S. Kelly, A. Riccardi, E. Marks, F. Magistri, T. Guadagnino, M. Chli, and C. Stachniss, “Target-Aware Implicit Mapping for Agricultural Crop Inspection,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Video] 
    @inproceedings{kelly2023icra,
author = {Shane Kelly and Alessandro Riccardi and Elias Marks and Federico Magistri and Tiziano Guadagnino and Margarita Chli and Cyrill Stachniss},
title = {{Target-Aware Implicit Mapping for Agricultural Crop Inspection}},
booktitle = icra,
year = 2023,
videourl = {https://youtu.be/UAIqn0QnpKg}
}
  • A. Riccardi, S. Kelly, E. Marks, F. Magistri, T. Guadagnino, J. Behley, M. Bennewitz, and C. Stachniss, “Fruit Tracking Over Time Using High-Precision Point Clouds,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Video] 
    @inproceedings{riccardi2023icra,
author = {Alessandro Riccardi and Shane Kelly and Elias Marks and Federico Magistri and Tiziano Guadagnino and Jens Behley and Maren Bennewitz and Cyrill Stachniss},
title = {{Fruit Tracking Over Time Using High-Precision Point Clouds}},
booktitle = icra,
year = 2023,
videourl = {https://youtu.be/fBGSd0--PXY}
}
  • G. Roggiolani, M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{roggiolani2023icra-hajs,
author = {G. Roggiolani and M. Sodano and F. Magistri and T. Guadagnino and J. Behley and C. Stachniss},
title = {{Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain}},
booktitle = icra,
year = {2023},
codeurl = {https://github.com/PRBonn/HAPT},
videourl = {https://youtu.be/miuOJjxlJic}
}
  • G. Roggiolani, F. Magistri, T. Guadagnino, J. Weyler, G. Grisetti, C. Stachniss, and J. Behley, “On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{roggiolani2023icra-odsp,
author = {G. Roggiolani and F. Magistri and T. Guadagnino and J. Weyler and G. Grisetti and C. Stachniss and J. Behley},
title = {{On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics}},
booktitle = icra,
year = 2023,
codeurl= {https://github.com/PRBonn/agri-pretraining},
videourl = {https://youtu.be/FDWY_UnfsBs}
}
  • F. Stache, J. Westheider, F. Magistri, C. Stachniss, and M. Popovic, “Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation,” Robotics and autonomous systems, vol. 159, p. 104288, 2023. doi:10.1016/j.robot.2022.104288
    [BibTeX] [PDF] 
    @article{stache2023jras,
author = {F. Stache and J. Westheider and F. Magistri and C. Stachniss and M. Popovic},
title = {{Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation}},
journal = jras,
volume = {159},
pages = {104288},
year = {2023},
issn = {0921-8890},
doi = {10.1016/j.robot.2022.104288},
}

2022

  • J. Rückin, L. Jin, F. Magistri, C. Stachniss, and M. Popović, “Informative Path Planning for Active Learning in Aerial Semantic Mapping,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2022.
    [BibTeX] [PDF] 
    @InProceedings{rueckin2022iros,
author = {J. R{\"u}ckin and L. Jin and F. Magistri and C. Stachniss and M. Popovi\'c},
title = {{Informative Path Planning for Active Learning in Aerial Semantic Mapping}},
booktitle = iros,
year = {2022},
}
  • F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. Läbe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, “Contrastive 3d shape completion and reconstruction for agricultural robots using rgb-d frames,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 4, pp. 10120-10127, 2022.
    [BibTeX] [PDF] [Video] 
    @article{magistri2022ral-iros,
author = {Federico Magistri and Elias Marks and Sumanth Nagulavancha and Ignacio Vizzo and Thomas L{\"a}be and Jens Behley and Michael Halstead and Chris McCool and Cyrill Stachniss},
title = {Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB-D Frames},
journal = ral,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2022ral-iros.pdf},
year = {2022},
volume={7},
number={4},
pages={10120-10127},
videourl = {https://www.youtube.com/watch?v=2ErUf9q7YOI},
}
  • E. Marks, F. Magistri, and C. Stachniss, “Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2022.
    [BibTeX] [PDF] 
    @inproceedings{marks2022icra,
author = {E. Marks and F. Magistri and C. Stachniss},
title = {{Precise 3D Reconstruction of Plants from UAV Imagery Combining Bundle Adjustment and Template Matching}},
booktitle = icra,
year = 2022,
}
  • J. Weyler, F. Magistri, P. Seitz, J. Behley, and C. Stachniss, “In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation,” in Proc. of the winter conf. on applications of computer vision (wacv), 2022.
    [BibTeX] [PDF] 
    @inproceedings{weyler2022wacv,
author = {J. Weyler and F. Magistri and P. Seitz and J. Behley and C. Stachniss},
title = {{In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation}},
booktitle = wacv,
year = 2022,
}

2021

  • D. Schunck, F. Magistri, R. A. Rosu, A. Cornelißen, N. Chebrolu, S. Paulus, J. Léon, S. Behnke, C. Stachniss, H. Kuhlmann, and L. Klingbeil, “Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis ,” Plos one, vol. 16, iss. 8, pp. 1-18, 2021. doi:10.1371/journal.pone.0256340
    [BibTeX] [PDF]
    Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
    @article{schunck2021plosone,
author = {D. Schunck and F. Magistri and R.A. Rosu and A. Corneli{\ss}en and N. Chebrolu and S. Paulus and J. L\'eon and S. Behnke and C. Stachniss and H. Kuhlmann and L. Klingbeil},
title = {{Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis
}},
journal = plosone,
year = 2021,
url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0256340&type=printable},
volume = {16},
number = {8},
doi = {10.1371/journal.pone.0256340},
pages = {1-18},
abstract = {Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.},
}
  • F. Stache, J. Westheider, F. Magistri, M. Popović, and C. Stachniss, “Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation,” in Proc. of the european conf. on mobile robots (ecmr), 2021.
    [BibTeX] [PDF] 
    @InProceedings{stache2021ecmr,
author = {F. Stache and J. Westheider and F. Magistri and M. Popovi\'c and C. Stachniss},
title = {{Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation}},
booktitle = ecmr,
year = {2021},
}
  • F. Magistri, N. Chebrolu, J. Behley, and C. Stachniss, “Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Video] 
    @inproceedings{magistri2021icra,
author = {F. Magistri and N. Chebrolu and J. Behley and C. Stachniss},
title = {{Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss}},
booktitle = icra,
year = 2021,
videourl = {https://youtu.be/MF2A4ihY2lE},
}
  • N. Chebrolu, F. Magistri, T. Läbe, and C. Stachniss, “Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping,” Plos one, vol. 16, iss. 2, 2021.
    [BibTeX] [PDF] [Video] 
    @article{chebrolu2021plosone,
author = {N. Chebrolu and F. Magistri and T. L{\"a}be and C. Stachniss},
title = {{Registration of Spatio-Temporal Point Clouds of Plants for Phenotyping}},
journal = plosone,
year = 2021,
volume = 16,
number = 2,
videourl = {https://youtu.be/OV39kb5Nqg8},
}
  • C. Carbone, D. Albani, F. Magistri, D. Ognibene, C. Stachniss, G. Kootstra, D. Nardi, and V. Trianni, “Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain,” in Proc. of the intl. symp. on distributed autonomous robotic systems (dars), 2021.
    [BibTeX] [PDF] 
    @inproceedings{carbone2021dars,
author = {C. Carbone and D. Albani and F. Magistri and D. Ognibene and C. Stachniss and G. Kootstra and D. Nardi and V. Trianni},
title = {{Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain}},
booktitle = dars,
year = 2021,
}

2020

  • F. Magistri, N. Chebrolu, and C. Stachniss, “Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2020.
    [BibTeX] [PDF] [Video] 
    @inproceedings{magistri2020iros,
author = {F. Magistri and N. Chebrolu and C. Stachniss},
title = {{Segmentation-Based 4D Registration of Plants Point Clouds for Phenotyping}},
booktitle = iros,
year = {2020},
url={https://www.ipb.uni-bonn.de/pdfs/magistri2020iros.pdf},
videourl = {https://youtu.be/OV39kb5Nqg8},
}