Barrios, ThéoGerhards, JulienPrévost, StéphanieLoscos, CelineSauvage, BasileHasic-Telalovic, Jasminka2022-04-222022-04-222022978-3-03868-171-71017-4656https://doi.org/10.2312/egp.20221007https://diglib.eg.org:443/handle/10.2312/egp20221007Recently, 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies --> Computational photography; 3D imaging; Neural networks; ReconstructionComputing methodologiesComputational photography3D imagingNeural networksReconstruction10.2312/egp.2022100717-182 pages