Lucas Nunes

PhD Student
Contact:
Email: lucas.nunes@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 29 05
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.003
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: Google Scholar | ResearchGate | LinkedIn

Research Interests

  • Autonomous Vehicles
  • Representation Learning
  • Self-Supervised Learning
  • Computer Vision

Short CV

Lucas Nunes is a Ph.D. student at the University of Bonn. He received a Bachelor’s degree in Computer Science and a Master’s degree in Robotics from the University of São Paulo, Brazil, in 2018 and 2020, respectively. During his Master’s he spent three months at Karlsruher Institut für Technologie as an internship student. His Master’s thesis was focused on Depth Estimation of Occluded Regions.

Code releases

Publications

2022

  • L. Nunes, X. Chen, R. Marcuzzi, A. Osep, L. Leal-Taixé, C. Stachniss, and J. Behley, “Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles,” IEEE Robotics and Automation Letters (RA-L), 2022. doi:10.1109/LRA.2022.3187872
    [BibTeX] [PDF] [Code]
    @article{nunes2022ral-3duis,
    author = {Lucas Nunes and Xieyuanli Chen and Rodrigo Marcuzzi and Aljosa Osep and Laura Leal-Taixé and Cyrill Stachniss and Jens Behley},
    title = {{Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles}},
    journal = ral,
    url = {https://www.ipb.uni-bonn.de/pdfs/nunes2022ral-iros.pdf},
    codeurl = {https://github.com/PRBonn/3DUIS},
    doi = {10.1109/LRA.2022.3187872},
    year = 2022
    }
  • 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},
    }
  • 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},
    }
  • L. Nunes, R. Marcuzzi, X. Chen, J. Behley, and C. Stachniss, “SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 2, pp. 2116-2123, 2022. doi:10.1109/LRA.2022.3142440
    [BibTeX] [PDF] [Code]
    @article{nunes2022ral,
    author = {L. Nunes and R. Marcuzzi and X. Chen and J. Behley and C. Stachniss},
    title = {{SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination}},
    journal = {IEEE Robotics and Automation Letters (RA-L)},
    year = 2022,
    doi = {10.1109/LRA.2022.3142440},
    issn = {2377-3766},
    volume = {7},
    number = {2},
    pages = {2116-2123},
    url = {http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf},
    codeurl = {https://github.com/PRBonn/segcontrast}
    }
  • 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},
    }