CV

Extended cv

Education

• Ph.D, University of Bonn, 2026 (expected)
• M.S. in Computer Science, University of São Paulo, 2020
• B.S. in Computer Science, University of São Paulo, 2018

Work experience

• Apr/2020 - Nov/2020: Software Engineer
  • IVEX
  • Duties includes: Development of front and back-end for a safety assessment tool and urban simulated environments in the context of autonomous vehicles.
• Jan/2019 - Apr/2020: Technical Leader
  • Juristec+
  • Duties included: Coordination of a web crawler programming team for data collection, and development/maintenance of a back-end database server.
• Jan/2018 - Aug/2018: Python Developer
  • Juristec+
  • Duties included: Development of web crawler Python scripts for data collection.

Skills

• Languages:
  • Portuguese: Native
  • English: Advanced
  • German: Beginner
• Programming:
  • C
  • Python
  • Java
  • R
  • Matlab
• Technical experience
  • Unix-based operating system
  • Robot Operating System (ROS)
  • Parallel programming
  • SQL/NoSQL Database

Publications

Tree Skeletonization from 3D Point Clouds by Denoising Diffusion

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.

Zero‑Shot Semantic Segmentation for Robots in Agriculture

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.

SfmOcc: Vision-Based 3D Semantic Occupancy Prediction in Urban Environments

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

Towards Generating Realistic 3D Semantic Training Data for Autonomous Driving

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.

Joint Intrinsic and Extrinsic Calibration of Perception Systems Utilizing a Calibration Environment

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.

Open-World Semantic Segmentation Including Class Similarity

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.

Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion

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.

Mask4D: End-to-End Mask-Based 4D Panoptic Segmentation for LiDAR Sequences

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.

Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition

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.

Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving

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.

KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition

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.

ERASOR2: Instance-Aware Robust 3D Mapping of the Static World in Dynamic Scenes

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.

Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles

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.

Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions

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.

Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation

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.

SegContrast: 3D Point Cloud Feature Representation Learning through Self-supervised Segment Discrimination

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.

Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

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.

Environment reconstruction on disparity images using surface features and Generative Adversarial Networks

LP Nunes Matias, “Environment reconstruction on disparity images using surface features and Generative Adversarial Networks,” M.Sc. Thesis. University of São Paulo, 2020.

Environment reconstruction on depth images using Generative Adversarial Networks

LP Nunes Matias, JR Souza, DF Wolf, “Environment reconstruction on depth images using Generative Adversarial Networks,” arXiv preprint, arXiv:1912.03992, 2019.

VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal

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.

Talks

The Autonomous Vehicles Revolution: A New Relation Between User and Vehicle.

October 01, 2019

Panel at São Paulo Expo, São Paulo, Brasil

How Autonomous Vehicles can perceive what is happening around and make decisions?

March 01, 2018

Talk at ONOVOLAB, São Carlos, Brasil

Teaching

Introduction to Mobile Robotics

Advanced Techniques for Mobile Sensing and Robotics

Advanced Techniques for Mobile Sensing and Robotics

Techniques for Self-Driving Cars (Single Lecture)

Tutor: Machine Learning for Robotics and Computer Vision

Tutor: Machine Learning for Robotics and Computer Vision

From Perceptron to Generative Adversarial Networks: The Evolution of Neural Networks

Supervision

Novel View Synthesis of Indoor Dynamic Scenes

Open-World Panoptic Segmentation of Traffic Participants