Search Results for author: Alessio Tonioni

Found 17 papers, 8 papers with code

ParGAN: Learning Real Parametrizable Transformations

no code implementations9 Nov 2022 Diego Martin Arroyo, Alessio Tonioni, Federico Tombari

Current methods for image-to-image translation produce compelling results, however, the applied transformation is difficult to control, since existing mechanisms are often limited and non-intuitive.

Image-to-Image Translation Translation

LegoFormer: Transformers for Block-by-Block Multi-view 3D Reconstruction

1 code implementation23 Jun 2021 Farid Yagubbayli, Yida Wang, Alessio Tonioni, Federico Tombari

Most modern deep learning-based multi-view 3D reconstruction techniques use RNNs or fusion modules to combine information from multiple images after independently encoding them.

3D Reconstruction Object Reconstruction

Unsupervised Novel View Synthesis from a Single Image

no code implementations5 Feb 2021 Pierluigi Zama Ramirez, Alessio Tonioni, Federico Tombari

Novel view synthesis from a single image aims at generating novel views from a single input image of an object.

Novel View Synthesis

Batch Normalization Embeddings for Deep Domain Generalization

no code implementations25 Nov 2020 Mattia Segu, Alessio Tonioni, Federico Tombari

Several recent methods use multiple datasets to train models to extract domain-invariant features, hoping to generalize to unseen domains.

Domain Generalization

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 Pose Estimation

Continual Adaptation for Deep Stereo

1 code implementation10 Jul 2020 Matteo Poggi, Alessio Tonioni, Fabio Tosi, Stefano Mattoccia, Luigi Di Stefano

Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression.

Depth Estimation

Unsupervised Domain Adaptation for Depth Prediction from Images

1 code implementation9 Sep 2019 Alessio Tonioni, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano

Extensive experimental results based on standard datasets and evaluation protocols prove that our technique can address effectively the domain shift issue with both stereo and monocular depth prediction architectures and outperforms other state-of-the-art unsupervised loss functions that may be alternatively deployed to pursue domain adaptation.

Depth Estimation Depth Prediction +1

Semi-Automatic Labeling for Deep Learning in Robotics

1 code implementation5 Aug 2019 Daniele De Gregorio, Alessio Tonioni, Gianluca Palli, Luigi Di Stefano

In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen to define initial object bounding box, to create large labeled datasets with minimal human intervention.

object-detection Object Detection

Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC

no code implementations17 Jul 2019 Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi Di Stefano, Simon Walker, Philip H. S. Torr

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs.

Learning to Adapt for Stereo

1 code implementation CVPR 2019 Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.

Autonomous Driving Stereo Depth Estimation

Domain invariant hierarchical embedding for grocery products recognition

no code implementations2 Feb 2019 Alessio Tonioni, Luigi Di Stefano

Moreover, there exist a significant domain shift between the images that should be recognized at test time, taken in stores by cheap cameras, and those available for training, usually just one or a few studio-quality images per product.

Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation

no code implementations13 Oct 2018 Pierluigi Zama Ramirez, Alessio Tonioni, Luigi Di Stefano

To prove the effectiveness of our proposal, we show how a semantic segmentation CNN trained on images from the synthetic GTA dataset adapted by our method can improve performance by more than 16% mIoU with respect to the same model trained on synthetic images.

Domain Adaptation Semantic Segmentation +1

Real-time self-adaptive deep stereo

1 code implementation CVPR 2019 Alessio Tonioni, Fabio Tosi, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs.

Stereo Depth Estimation

A deep learning pipeline for product recognition on store shelves

no code implementations3 Oct 2018 Alessio Tonioni, Eugenio Serra, Luigi Di Stefano

Then, available product databases usually include just one or a few studio-quality images per product (referred to herein as reference images), whilst at test time recognition is performed on pictures displaying a portion of a shelf containing several products and taken in the store by cheap cameras (referred to as query images).

Image Retrieval object-detection +2

Unsupervised Adaptation for Deep Stereo

1 code implementation ICCV 2017 Alessio Tonioni, Matteo Poggi, Stefano Mattoccia, Luigi Di Stefano

Recent ground-breaking works have shown that deep neural networks can be trained end-to-end to regress dense disparity maps directly from image pairs.

Product recognition in store shelves as a sub-graph isomorphism problem

no code implementations26 Jul 2017 Alessio Tonioni, Luigi Di Stefano

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy.

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