Search Results for author: Filippo Bergamasco

Found 6 papers, 1 papers with code

A Geometric Model for Polarization Imaging on Projective Cameras

no code implementations29 Nov 2022 Mara Pistellato, Filippo Bergamasco

The vast majority of Shape-from-Polarization (SfP) methods work under the oversimplified assumption of using orthographic cameras.

Demosaicking

Deep Demosaicing for Polarimetric Filter Array Cameras

1 code implementation24 Nov 2022 Mara Pistellato, Filippo Bergamasco, Tehreem Fatima, Andrea Torsello

Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner.

Demosaicking

On-the-go Reflectance Transformation Imaging with Ordinary Smartphones

no code implementations18 Oct 2022 Mara Pistellato, Filippo Bergamasco

Reflectance Transformation Imaging (RTI) is a popular technique that allows the recovery of per-pixel reflectance information by capturing an object under different light conditions.

Parameter-Free Lens Distortion Calibration of Central Cameras

no code implementations ICCV 2017 Filippo Bergamasco, Luca Cosmo, Andrea Gasparetto, Andrea Albarelli, Andrea Torsello

At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process.

Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction

no code implementations CVPR 2015 Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodola, Daniel Cremers

This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters.

3D Reconstruction 3D Shape Reconstruction

Can a Fully Unconstrained Imaging Model Be Applied Effectively to Central Cameras?

no code implementations CVPR 2013 Filippo Bergamasco, Andrea Albarelli, Emanuele Rodola, Andrea Torsello

Traditional camera models are often the result of a compromise between the ability to account for non-linearities in the image formation model and the need for a feasible number of degrees of freedom in the estimation process.

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