no code implementations • 20 Dec 2023 • Pierluigi Zama Ramirez, Luca De Luigi, Daniele Sirocchi, Adriano Cardace, Riccardo Spezialetti, Francesco Ballerini, Samuele Salti, Luigi Di Stefano
In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes.
no code implementations • CVPR 2023 • Marco Toschi, Riccardo De Matteo, Riccardo Spezialetti, Daniele De Gregorio, Luigi Di Stefano, Samuele Salti
By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset.
no code implementations • 10 Feb 2023 • Luca De Luigi, Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes.
no code implementations • 15 Oct 2022 • Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
In contrast, in this work, we focus on obtaining a discriminative feature space for the target domain enforcing consistency between a point cloud and its augmented version.
1 code implementation • 21 Oct 2021 • Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations.
no code implementations • 6 Jun 2021 • Janis Postels, Mengya Liu, Riccardo Spezialetti, Luc van Gool, Federico Tombari
Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time.
no code implementations • 17 Nov 2020 • Riccardo Spezialetti, David Joseph Tan, Alessio Tonioni, Keisuke Tateno, Federico Tombari
Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning.
1 code implementation • NeurIPS 2020 • Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano
In this work, we show the feasibility of learning a robust canonical orientation for surfaces represented as point clouds.
no code implementations • 6 Nov 2019 • Marlon Marcon, Riccardo Spezialetti, Samuele Salti, Luciano Silva, Luigi Di Stefano
Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications.
no code implementations • ICCV 2019 • Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano
Establishing correspondences between 3D shapes is a fundamental task in 3D Computer Vision, typically addressed by matching local descriptors.
no code implementations • 15 Sep 2019 • Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano
Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features.
no code implementations • 22 Jul 2017 • Julian Zilly, Amit Boyarski, Micael Carvalho, Amir Atapour Abarghouei, Konstantinos Amplianitis, Aleksandr Krasnov, Massimiliano Mancini, Hernán Gonzalez, Riccardo Spezialetti, Carlos Sampedro Pérez, Hao Li
Reviewing this project with modern eyes provides us with the opportunity to reflect on several issues, relevant now as then to the field of computer vision and research in general, that go beyond the technical aspects of the work.
no code implementations • ICCV 2015 • Samuele Salti, Federico Tombari, Riccardo Spezialetti, Luigi Di Stefano
Keypoint detection represents the first stage in the majority of modern computer vision pipelines based on automatically established correspondences between local descriptors.