Ignacio Vizzo

PhD Student
Contact:
Email: ivizzo@nulluni-bonn.de
Tel: +49 – 228 – 73 – 27 13
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.009
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

  • 3D LiDAR-based SLAM
  • 3D Surface Reconstruction
  • Localization and Mapping in Outdoor Environments
  • High-performance Computing

Short CV

Ignacio Vizzo is a Research Assistant and Ph.D. Student at the University of Bonn since January 2019. He received his Electrical Engineering Degree from Universidad Nacional de Rosario, Argentina in December 2015. In the 2 years preceding his Ph.D. he worked for iRobot (USA) on Software development, developing behaviors, and working on the navigation system for lawn-care consumer robots. Before starting in the robotics world, he worked for 2 years in the Energy Industry, doing field testing in diverse power generation plants (hydroelectric, solar, etc). During this time he developed a novel 3D-SVPWM algorithm to control a 3-phase power inverter.

Teaching

  • Modern C++ for Image Processing, 2019
  • Photogrammetry and remote sensing – 2019/2020
  • Sensors and state estimation – 2019/2020
  • Modern C++ for Image Processing, 2020 (Website/)

Publications

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},
    }

  • 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},
    }

  • B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, “Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, p. 7503–7510, 2022. doi:10.1109/LRA.2022.3183245
    [BibTeX] [PDF] [Code] [Video]
    @article{mersch2022ral,
    author = {B. Mersch and X. Chen and I. Vizzo and L. Nunes and J. Behley and C. Stachniss},
    title = {{Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions}},
    journal = ral,
    year = 2022,
    url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2022ral.pdf},
    volume = {7},
    number = {3},
    pages = {7503--7510},
    doi = {10.1109/LRA.2022.3183245},
    codeurl = {https://github.com/PRBonn/4DMOS},
    videourl = {https://youtu.be/5aWew6caPNQ},
    }

  • 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},
    }

  • X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, “Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 6107-6114, 2022. doi:10.1109/LRA.2022.3166544
    [BibTeX] [PDF] [Code]
    @article{chen2022ral,
    author = {X. Chen and B. Mersch and L. Nunes and R. Marcuzzi and I. Vizzo and J. Behley and C. Stachniss},
    title = {{Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    volume = 7,
    number = 3,
    pages = {6107-6114},
    url = {http://arxiv.org/pdf/2201.04501},
    issn = {2377-3766},
    doi = {10.1109/LRA.2022.3166544},
    codeurl = {https://github.com/PRBonn/auto-mos},
    }

  • I. Vizzo, T. Guadagnino, J. Behley, and C. Stachniss, “VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data,” Sensors, vol. 22, iss. 3, 2022. doi:10.3390/s22031296
    [BibTeX] [PDF] [Code]
    @article{vizzo2022sensors,
    author = {Vizzo, I. and Guadagnino, T. and Behley, J. and Stachniss, C.},
    title = {VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data},
    journal = {Sensors},
    volume = {22},
    year = {2022},
    number = {3},
    article-number = {1296},
    url = {https://www.mdpi.com/1424-8220/22/3/1296},
    issn = {1424-8220},
    doi = {10.3390/s22031296},
    codeurl = {https://github.com/PRBonn/vdbfusion},
    }

  • 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

  • I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, “Poisson Surface Reconstruction for LiDAR Odometry and Mapping,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{vizzo2021icra,
    author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss},
    title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}},
    booktitle = icra,
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/vizzo2021icra.pdf},
    codeurl = {https://github.com/PRBonn/puma},
    videourl = {https://youtu.be/7yWtYWaO5Nk}
    }

  • X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, “Range Image-based LiDAR Localization for Autonomous Vehicles,” in Proc. of the IEEE Int. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{chen2021icra,
    author = {X. Chen and I. Vizzo and T. L{\"a}be and J. Behley and C. Stachniss},
    title = {{Range Image-based LiDAR Localization for Autonomous Vehicles}},
    booktitle = icra,
    year = 2021,
    url = {http://www.ipb.uni-bonn.de/pdfs/chen2021icra.pdf},
    codeurl = {https://github.com/PRBonn/range-mcl},
    videourl = {https://youtu.be/hpOPXX9oPqI},
    }

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.},
    }

2019

  • A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet++: Fast and Accurate LiDAR Semantic Segmentation,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2019.
    [BibTeX] [PDF] [Code] [Video]
    @inproceedings{milioto2019iros,
    author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
    title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
    booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2019,
    codeurl = {https://github.com/PRBonn/lidar-bonnetal},
    videourl = {https://youtu.be/wuokg7MFZyU},
    }