Fast and Fine Disparity Reconstruction for Wide-baseline Camera Arrays with Deep Neural Networks

Abstract
Recently, disparity-based 3D reconstruction for stereo camera pairs and light field cameras have been greatly improved with the uprising of deep learning-based methods. However, only few of these approaches address wide-baseline camera arrays which require specific solutions. In this paper, we introduce a deep-learning based pipeline for multi-view disparity inference from images of a wide-baseline camera array. The network builds a low-resolution disparity map and retains the original resolution with an additional up scaling step. Our solution successfully answers to wide-baseline array configurations and infers disparity for full HD images at interactive times, while reducing quantification error compared to the state of the art.
Description

CCS Concepts: Computing methodologies --> Computational photography; 3D imaging; Neural networks; Reconstruction

        
@inproceedings{
10.2312:egp.20221007
, booktitle = {
Eurographics 2022 - Posters
}, editor = {
Sauvage, Basile
and
Hasic-Telalovic, Jasminka
}, title = {{
Fast and Fine Disparity Reconstruction for Wide-baseline Camera Arrays with Deep Neural Networks
}}, author = {
Barrios, Théo
and
Gerhards, Julien
and
Prévost, Stéphanie
and
Loscos, Celine
}, year = {
2022
}, publisher = {
The Eurographics Association
}, ISSN = {
1017-4656
}, ISBN = {
978-3-03868-171-7
}, DOI = {
10.2312/egp.20221007
} }
Citation