Publications

• L. Nunes, R. Marcuzzi, J. Behley, and C. Stachniss, “Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2026. [Paper]
• R. Marcuzzi, L. Nunes, E. A. Marks, X. Zhong, J. Behley, and C. Stachniss, “Vision-Based Panoptic Occupancy Prediction in Urban Environments,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2026. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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), vol. 10, iss. 5, pp. 5074-5081, 2025. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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), vol. 7, iss. 4, pp. 8713-8720,2022. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• 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. [Paper]
• LP Nunes Matias, “Environment reconstruction on disparity images using surface features and Generative Adversarial Networks,” M.Sc. Thesis. University of São Paulo, 2020. [Paper]
• LP Nunes Matias, JR Souza, DF Wolf, “Environment reconstruction on depth images using Generative Adversarial Networks,” arXiv preprint, arXiv:1912.03992, 2019. [Paper]
• 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. [Paper]