StachnissLab

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 Rheinische Friedrich-Wilhelms-Universität Bonn since November 2019. 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 Albert-Ludwigs-Universität Freiburg as an Erasmus student.

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 in Precision Agriculture, Summer Semester 2020
• Master Project – Informative Path Planning in Precision Agriculture, Winter Semester 2020
• 3D Coordinate Systems – Winter Semester 2020

Publications

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 int. 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 int. 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={http://www.ipb.uni-bonn.de/pdfs/magistri2020iros.pdf},
  videourl = {https://youtu.be/OV39kb5Nqg8},
  }