Search Results for author: Riccardo Spezialetti

Found 13 papers, 2 papers with code

Deep Learning on 3D Neural Fields

no code implementations20 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.

ReLight My NeRF: A Dataset for Novel View Synthesis and Relighting of Real World Objects

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.

Image Relighting Novel View Synthesis

Deep Learning on Implicit Neural Representations of Shapes

no code implementations10 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.

Self-Distillation for Unsupervised 3D Domain Adaptation

no code implementations15 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.

Classification Point Cloud Classification +2

Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction

no code implementations6 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.

Data Augmentation Point Cloud Generation

A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views

no code implementations17 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.

3D Shape Reconstruction Object +1

Learning to Orient Surfaces by Self-supervised Spherical CNNs

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.

Boosting Object Recognition in Point Clouds by Saliency Detection

no code implementations6 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.

Object Recognition Saliency Detection

Learning an Effective Equivariant 3D Descriptor Without Supervision

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.

Performance Evaluation of Learned 3D Features

no code implementations15 Sep 2019 Riccardo Spezialetti, Samuele Salti, Luigi Di Stefano

Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features.

Object Recognition

Inspiring Computer Vision System Solutions

no code implementations22 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.

Learning a Descriptor-Specific 3D Keypoint Detector

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.

Binary Classification Keypoint Detection

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