no code implementations • 6 Dec 2023 • Luigi Riz, Cristiano Saltori, Yiming Wang, Elisa Ricci, Fabio Poiesi
Firstly, it introduces the novel task of NCD for point cloud semantic segmentation.
1 code implementation • 28 Aug 2023 • Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Fabio Poiesi, Elisa Ricci
In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.
1 code implementation • ICCV 2023 • Cristiano Saltori, Aljoša Ošep, Elisa Ricci, Laura Leal-Taixé
To answer this question, we design the first experimental setup for studying domain generalization (DG) for LiDAR semantic segmentation (DG-LSS).
1 code implementation • CVPR 2023 • Luigi Riz, Cristiano Saltori, Elisa Ricci, Fabio Poiesi
Firstly, we address the new problem of NCD for point cloud semantic segmentation.
1 code implementation • 17 Oct 2022 • Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang, Elisa Ricci, Nicu Sebe
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
1 code implementation • 6 Oct 2022 • Guofeng Mei, Cristiano Saltori, Fabio Poiesi, Jian Zhang, Elisa Ricci, Nicu Sebe, Qiang Wu
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
2 code implementations • 20 Jul 2022 • Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Elisa Ricci, Fabio Poiesi
We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.
2 code implementations • 20 Jul 2022 • Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuilière, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, Fabio Poiesi
Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds.
1 code implementation • 16 Oct 2020 • Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe, Elisa Ricci, Fabio Galasso
In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).
no code implementations • 1 Jan 2020 • Jurandy Almeida, Cristiano Saltori, Paolo Rota, Nicu Sebe
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.
1 code implementation • 3 Oct 2019 • Francesco Marra, Cristiano Saltori, Giulia Boato, Luisa Verdoliva
Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones.
no code implementations • 15 May 2019 • Cristiano Saltori, Subhankar Roy, Nicu Sebe, Giovanni Iacca
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.