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Giuseppe Fiameni

2 accepted papers

2022

CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

ECCV 2022poster

"3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain…

2022

GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation

ECCV 2022poster

"3D point cloud semantic segmentation is fundamental for autonomous driving. Most approaches in the literature neglect an important aspect, i.e., how to deal with domain shift when handling dynamic scenes. This can significantly hinder the navigation capabilities of self-driving vehicles. This paper…