1 code implementation • 28 Mar 2024 • Matteo Caligiuri, Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh
The acquisition of objects outside the Line-of-Sight of cameras is a very intriguing but also extremely challenging research topic.
no code implementations • 13 Sep 2023 • Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh
In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images.
no code implementations • 25 May 2022 • Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti, Cynthia Ifeyinwa Ugwu, Adriano Simonetto, Fabien Cardinaux, Pietro Zanuttigh
We also achieve low memory footprint for weights and activations by means of mixed precision quantization-at-training techniques.
no code implementations • 29 Nov 2021 • Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh, Henrik Schäfer
Indirect Time-of-Flight cameras (iToF) are low-cost devices that provide depth images at an interactive frame rate.
no code implementations • 14 Jan 2020 • Marco Toldo, Umberto Michieli, Gianluca Agresti, Pietro Zanuttigh
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data.
no code implementations • 2 Sep 2019 • Umberto Michieli, Matteo Biasetton, Gianluca Agresti, Pietro Zanuttigh
A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance.
no code implementations • CVPR 2019 • Gianluca Agresti, Henrik Schaefer, Piergiorgio Sartor, Pietro Zanuttigh
A Coarse-Fine CNN, able to exploit multi-frequency ToF data for MPI correction, is trained on synthetic data with ground truth in a supervised way.