DEEP MULTI-SCALE ARCHITECTURES FOR MONOCULAR DEPTH ESTIMATION

Michel Moukari 1, 2 Sylvaine Picard 2 Loic Simon 1 Frédéric Jurie 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CNN architectures, designed to explicitly make use of multi-scale features along the network, and compare them to a state-of-the-art single-scale approach. The paper also shows that involving multi-scale features in depth estimation not only improves the performance in terms of accuracy, but also gives qualitatively better depth maps. Experiments are done on the widely used NYU Depth dataset, on which the proposed method achieves state-of-the-art performance.
Type de document :
Communication dans un congrès
IEEE International Conference on Image Processing, Oct 2018, Athens, Greece
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https://hal.archives-ouvertes.fr/hal-01803817
Contributeur : Frederic Jurie <>
Soumis le : jeudi 31 mai 2018 - 06:41:09
Dernière modification le : jeudi 7 février 2019 - 14:46:42
Document(s) archivé(s) le : samedi 1 septembre 2018 - 12:39:31

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  • HAL Id : hal-01803817, version 1
  • ARXIV : 1806.03051

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Michel Moukari, Sylvaine Picard, Loic Simon, Frédéric Jurie. DEEP MULTI-SCALE ARCHITECTURES FOR MONOCULAR DEPTH ESTIMATION. IEEE International Conference on Image Processing, Oct 2018, Athens, Greece. 〈hal-01803817〉

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