no code implementations • 11 Apr 2024 • Francesco Ballerini, Pierluigi Zama Ramirez, Roberto Mirabella, Samuele Salti, Luigi Di Stefano
Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage.
no code implementations • 4 Apr 2024 • Alex Costanzino, Pierluigi Zama Ramirez, Mirko Del Moro, Agostino Aiezzo, Giuseppe Lisanti, Samuele Salti, Luigi Di Stefano
Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control.
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 • 6 Dec 2023 • Francesca Boccardi, Axel Saalbach, Heinrich Schulz, Samuele Salti, Ilyas Sirazitdinov
Chest X-ray (CXR) is frequently employed in emergency departments and intensive care units to verify the proper placement of central lines and tubes and to rule out related complications.
1 code implementation • 2 Oct 2023 • Adriano Cardace, Pierluigi Zama Ramirez, Francesco Ballerini, Allan Zhou, Samuele Salti, Luigi Di Stefano
While processing a field with the same reconstruction quality, we achieve task performance far superior to frameworks that process large MLPs and, for the first time, almost on par with architectures handling explicit representations.
no code implementations • 14 Sep 2023 • Andrea Amaduzzi, Giuseppe Lisanti, Samuele Salti, Luigi Di Stefano
The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark that we publicly release to foster research on text-to-shape coherence of text-conditioned 3D generative models.
1 code implementation • 27 Aug 2023 • Giovanni Minelli, Matteo Poggi, Samuele Salti
In this paper, we tackle the problem of generating a novel image from an arbitrary viewpoint given a single frame as input.
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.
1 code implementation • 6 Apr 2023 • Adriano Cardace, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality.
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 • 26 Jan 2023 • Pierluigi Zama Ramirez, Adriano Cardace, Luca De Luigi, Alessio Tonioni, Samuele Salti, Luigi Di Stefano
Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework.
no code implementations • 19 Jan 2023 • Pierluigi Zama Ramirez, Alex Costanzino, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization.
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.
no code implementations • 1 Sep 2022 • Matteo Poggi, Pierluigi Zama Ramirez, Fabio Tosi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation.
no code implementations • CVPR 2022 • Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We address the problem of registering synchronized color (RGB) and multi-spectral (MS) images featuring very different resolution by solving stereo matching correspondences.
1 code implementation • 10 Jun 2022 • Gianluca Berardi, Luca De Luigi, Samuele Salti, Luigi Di Stefano
In particular, we show that it is possible to use representation learning to learn a fixed-size, low-dimensional embedding space of trained deep models and that such space can be explored by interpolation or optimization to attain ready-to-use models.
no code implementations • CVPR 2022 • Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities.
1 code implementation • 28 Oct 2021 • Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones.
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.
1 code implementation • 13 Oct 2021 • Adriano Cardace, Luca De Luigi, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
We further rely on depth to generate a large and varied set of samples to Self-Train the final model.
1 code implementation • 6 Oct 2021 • Adriano Cardace, Pierluigi Zama Ramirez, Samuele Salti, Luigi Di Stefano
Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation.
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.
1 code implementation • CVPR 2020 • Fabio Tosi, Filippo Aleotti, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Luigi Di Stefano, Stefano Mattoccia
Whole understanding of the surroundings is paramount to autonomous systems.
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 • 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 • 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.
2 code implementations • ICCV 2019 • Pierluigi Zama Ramirez, Alessio Tonioni, Samuele Salti, Luigi Di Stefano
Recent works have proven that many relevant visual tasks are closely related one to another.
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.
no code implementations • CVPR 2013 • Samuele Salti, Alessandro Lanza, Luigi Di Stefano
The paper conjectures and demonstrates that repeatable keypoints based on salient symmetries at different scales can be detected by a novel analysis grounded on the wave equation rather than the heat equation underlying traditional Gaussian scale-space theory.