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Poster De Conférence Année : 2022

Fast and fine disparity reconstruction for wide-baseline camera arrays with deep neural networks

Résumé

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.
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Dates et versions

hal-03664186 , version 1 (10-05-2022)

Identifiants

Citer

T Barrios, J Gerhards, S Prévost, C Loscos. Fast and fine disparity reconstruction for wide-baseline camera arrays with deep neural networks. Eurographics 2022, Apr 2022, Reims, France. ⟨10.2312/egp.20221007⟩. ⟨hal-03664186⟩

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