Giuseppe Vecchio

Orcid: 0000-0001-5009-4365

According to our database1, Giuseppe Vecchio authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
ControlMat: A Controlled Generative Approach to Material Capture.
ACM Trans. Graph., October, 2024

Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data.
Auton. Robots, April, 2024

Structured Pattern Expansion with Diffusion Models.
CoRR, 2024

StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning.
CoRR, 2024

MIDGARD: A Robot Navigation Simulator for Outdoor Unstructured Environments.
Proceedings of the European Robotics Forum 2024, 2024

Learning-Based Ground Vehicle Navigation in Outdoor Unstructured Environments.
Proceedings of the European Robotics Forum 2024, 2024

MatFuse: Controllable Material Generation with Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

MatSynth: A Modern PBR Materials Dataset.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
<i>MeT</i>: A graph transformer for semantic segmentation of 3D meshes.
Comput. Vis. Image Underst., October, 2023

MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes.
CoRR, 2023

2022
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments.
CoRR, 2022

Deep reinforcement learning for multi-agent interaction.
AI Commun., 2022

2021
SurfaceNet: Adversarial SVBRDF Estimation from a Single Image.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020
MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., 2020


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