LatentSwap3D: Semantic Edits on 3D Image GANs
Authors: Enis Simsar, Alessio Tonioni, Evin Pınar Örnek, Federico Tombari
Published in CVPR23, 2022
Semantic edits in the latent space of Nerf based gan.
Authors: Enis Simsar, Alessio Tonioni, Evin Pınar Örnek, Federico Tombari
Published in CVPR23, 2022
Semantic edits in the latent space of Nerf based gan.
Authors: Farid Yagubbayli, Alessio Tonioni and Federico Tombari
Published in Neurip21, 2021
Transformers for voxel based 3D reconstruction.
Authors: Pierluigi Zama Ramirez, Alessio Tonioni and Federico Tombari
Published in ICCV21, 2021
An unsupervised approach for novel view synthesis.
Authors: Mattia Segù, Alessio Tonioni and Federico Tombari
Published in Pattern Recognition, 2023
An embedding based approach for deep domain generalization.
Authors: Matteo Poggi, Alessio Tonioni, Fabio Tosi, Stefano Mattoccia and Luigi Di Stefano
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
In this paper we propose an etension of our real-time self adaptive deep stereo system.
Authors: Alessio Tonioni, Matteo Poggi, Stefano Mattoccia and Luigi Di Stefano
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
In this paper we extend our previous unsupervised adaptation approach to fine-tune a deep learning stereo or mono model without any ground-truth information.
Authors: Daniele De Gregorio, Alessio Tonioni, Gianluca Palli and Luigi Di Stefano
Published in IEEE Transactions on Automation Science and Engineering , 2019
In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method to create large labeled datasets with minimal human intervention
Authors: Alessio Tonioni and Luigi Di Stefano
Published in Computer Vision and Image Understanding, 2019
In this paper we introduce a” deep learning architecture to effectively learn embedddings relying only on few samples and in presence of domain shifts.
Authors: Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi Di Stefano, Simon Walker, and Philip HS Torr
Published in IEEE Transactions on Circuits and Systems II: Express Briefs , 2019
In this paper, we leverage a FPGA-CPU chip to propose a novel, sophisticated, stereo approach that combines the best features of SGM and ELAS-based methods to compute highly accurate dense depth in real time.
Authors: Alessio Tonioni, Samuele Salti, Federico Tombari, Riccardo Spezialetti and Luigi Di Stefano
Published in International Journal of Computer Vision, 2018
In this paper we learn a descriptor-specific 3D keypoint detector so as to optimize the end-to-end performance of a feature matching pipeline
Authors: Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni, Michael Niemeyer, Federico Tombari
Published in Arxive, 2023
Turning Tetx prompt into full 3D meshes
Authors: Fabio Tosi, Alessio Tonioni, Daniele De Gregorio, Matteo Poggi
Published in Arxive, 2023
NeRF makes great groundtruth for stereo systems.
Authors: Mohamad Shahbazi, Evangelos Ntavelis, Alessio Tonioni, Edo Collins, Danda Pani Paudel, Martin Danelljan, Luc Van Gool
Published in Arxive, 2023
StyleGan like network can approximate NerfGAN.
Authors: Diego Martin Arroyo, Alessio Tonioni, Federico Tombari
Published in Arxive, 2022
Parametrizable CycleGAN.
Authors: Riccardo Spezialetti, David Joseph Tan, Alessio Tonioni, Keisuke Tateno and Federico Tombari
Published in International Conference on 3D Vision, 2020
A deep learning pipeline for multi view reconstruction.
Authors: Pierluigi Zama Ramirez, Alessio Tonioni, Samuele Salti and Luigi Di Stefano
Published in International Conference on Computer Vision, 2019
In this work, we introduce a novel adaptation framework that can operate across both task and domains.
Authors: Alessio Tonioni, Fabio Tosi, Matteo Poggi, Stefano Mattoccia and Luigi Di Stefano
Published in Conference on Computer Vision and Pattern Recognition, 2019
In this paper we propose a real-time self adaptive deep stereo system.
Authors: Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, and Philip HS Torr.
Published in Conference on Computer Vision and Pattern Recognition, 2019
In this paper we introduce a” learning-to-adapt” framework that enables deep stereo methods to continuously adapt to new target domains in an unsupervised manner.
Authors: Pierluigi Zama Ramirez, Alessio Tonioni and Luigi Di Stefano
Published in International Conference on Image Processing, Applications and Systems, 2018
In this paper, we address the problem of domain adaptation for computer vision by learning a domain-to-domain image translation GAN. Peculiarly to our method, we introduce semantic constraints into the generation process to both avoid artifacts and guide the synthesis
Authors: Alessio Tonioni, Eugenio Serra and Luigi Di Stefano.
Published in International Conference on Image Processing, Applications and Systems, 2018
In this paper, we propose a deep learning pipeline to recognize products on grocery shelves that can scale effortlessly to thousand of different products to recognize.
Authors: Alessio Tonioni, Matteo Poggi, stefano Mattoccia and Luigi Di Stefano
Published in Proceedings of the IEEE International Conference on Computer Vision, 2017
In this paper we propose a novel unsupervised adaptation approach that enables to fine-tune a deep learning stereo model without any ground-truth information.
Authors: Alessio Tonioni and Luigi Di Stefano
Published in International Conference on Image Analysis and Processing, 2017
In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout.
Authors: Fabio Tosi, Matteo Poggi, Alessio Tonioni, Luigi Di Stefano, & Stefano Mattoccia
Published in 28th British Machine Vision Conference (BMVC 2017), 2017
In this paper we propose a methodology suited for training a confidence measure for stereo in a self-supervised manner.