Fast and Fine Disparity Reconstruction for Wide-baseline Camera Arrays with Deep Neural Networks
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Date
2022
Authors
Barrios, Théo
Gerhards, Julien
Prévost, Stéphanie
Loscos, Celine
Gerhards, Julien
Prévost, Stéphanie
Loscos, Celine
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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}
}