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publications
VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal
Published in IEEE Intelligent Vehicles Symposium (IV), 2019
Recommended citation: LP Nunes Matias, M. Sons, JR Souza, DF Wolf, C. Stiller, “VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal,” 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 2019, pp. 310-316.
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Environment reconstruction on depth images using Generative Adversarial Networks
Published in arXiv, 2019
Recommended citation: LP Nunes Matias, JR Souza, DF Wolf, “Environment reconstruction on depth images using Generative Adversarial Networks,” arXiv preprint, arXiv:1912.03992, 2019.
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Environment reconstruction on disparity images using surface features and Generative Adversarial Networks
Published in M.Sc. Thesis. University of São Paulo, 2020
Recommended citation: LP Nunes Matias, “Environment reconstruction on disparity images using surface features and Generative Adversarial Networks,” M.Sc. Thesis. University of São Paulo, 2020.
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Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes
Published in Proc. of Robotics: Science and Systems (RSS), 2022
Recommended citation: H. Lim, L. Nunes, B. Mersch, X. Chen, J. Behley, H. Myung, and C. Stachniss, “ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes,” in Proc. of Robotics: Science and Systems (RSS), 2023.
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KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition
Published in IEEE Robotics and Automation Letters (RA-L), 2022
Recommended citation: 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.
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Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Published in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2023
Recommended citation: 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.
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Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition
Published in Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops, 2023
Recommended citation: 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.
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Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences
Published in IEEE Robotics and Automation Letters (RA-L), 2023
Recommended citation: 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.
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Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion
Published in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024
Recommended citation: L. Nunes, R. Marcuzzi, B. Mersch, J. Behley, and C. Stachniss, “Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
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Open-World Semantic Segmentation Including Class Similarity
Published in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024
Recommended citation: 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.
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Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment
Published in IEEE Robotics and Automation Letters (RA-L), 2024
Recommended citation: 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.
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Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving
Published in arXiv, 2025
Recommended citation: L. Nunes, R. Marcuzzi, J. Behley, and C. Stachniss, “Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving,” arXiv Preprint, vol. arXiv:2503.21449, 2025.
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SfmOcc: Vision-Based 3D Semantic Occupancy Prediction in Urban Environments
Published in IEEE Robotics and Automation Letters (RA-L), 2025
Recommended citation: R. Marcuzzi, L. Nunes, E. A. Marks, L. Wiesmann, T. Läbe, J. Behley, and C. Stachniss, “SfmOcc: Vision-Based 3D Semantic Occupancy Prediction in Urban Environments,” IEEE Robotics and Automation Letters (RA-L), 2025. doi:10.1109/LRA.2025.3557227
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Zero‑Shot Semantic Segmentation for Robots in Agriculture
Published in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2025, 2025
Recommended citation: Y. Chong, L. Nunes, F. Magistri, X. Zhong, J. Behley, and C. Stachniss, “Zero‑Shot Semantic Segmentation for Robots in Agriculture,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2025.
Tree Skeletonization from 3D Point Clouds by Denoising Diffusion
Published in Proceedings of the IEEE/CVF Int. Conf. on Computer Vision (ICCV), 2025, 2025
Recommended citation: E. Marks, L. Nunes, F. Magistri, M. Sodano, R. Marcuzzi, L. Zimmermann, J. Behley, and C. Stachniss, “Tree Skeletonization from 3D Point Clouds by Denoising Diffusion,” in Proceedings of the IEEE/CVF Int. Conf. on Computer Vision (ICCV), 2025.
supervision
Open-World Panoptic Segmentation of Traffic Participants
University of Bonn, 2023
Supervised a M.Sc. Project with the title “Open-World Panoptic Segmentation of Traffic Participants”.
Novel View Synthesis of Indoor Dynamic Scenes
University of Zurich, 2024
Supervised the Master’s thesis of a student from the University of Zurich.
talks
teaching
From Perceptron to Generative Adversarial Networks: The Evolution of Neural Networks
Mini Course, University of São Paulo, 2019
Gave a mini course in the Institute of Mathematics and Computer Science in São Carlos, Brazil.
Tutor: Machine Learning for Robotics and Computer Vision
Master's program, University of Bonn, 2021
In charge of tutorials and assignments corrections for the Machine Learning for Robotics and Computer Vision from the University of Bonn.
Tutor: Machine Learning for Robotics and Computer Vision
Master's program, University of Bonn, 2022
In charge of tutorials and assignments corrections for the Machine Learning for Robotics and Computer Vision from the University of Bonn.
Techniques for Self-Driving Cars (Single Lecture)
Master's program, University of Bonn, 2023
Gave one lecture about “Unsupervised Learning” for the Techniques for Self-Driving Cars course at the University of Bonn.
Advanced Techniques for Mobile Sensing and Robotics
Master's program, University of Bonn, 2024
Shared teaching together with Prof. Dr. Cyrill Stachniss in the Advanced Techniques for Mobile Sensing and Robotics course.
Advanced Techniques for Mobile Sensing and Robotics
Master's program, University of Bonn, 2025
Responsible for the lectures of the Advanced Techniques for Mobile Sensing and Robotics course.
Introduction to Mobile Robotics
Master's program, University of Bonn, 2025
Shared teaching together with Prof. Dr. Cyrill Stachniss in the Introduction to Mobile Robotics course.