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Volume: 32 | Article ID: art00019
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Efficient Multilevel Architecture for Depth Estimation from a Single Image
  DOI :  10.2352/ISSN.2470-1173.2020.14.COIMG-377  Published OnlineJanuary 2020
Abstract

Monocular depth estimation is an important task in scene understanding with applications to pose, segmentation and autonomous navigation. Deep Learning methods relying on multilevel features are currently used for extracting local information that is used to infer depth from a single RGB image. We present an efficient architecture that utilizes the features from multiple levels with fewer connections compared to previous networks. Our model achieves comparable scores for monocular depth estimation with better efficiency on the memory requirements and computational burden.

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Bruno Artacho, Nilesh Pandey, Andreas Savakis, "Efficient Multilevel Architecture for Depth Estimation from a Single Imagein Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVIII,  2020,  pp 377-1 - 377-7,  https://doi.org/10.2352/ISSN.2470-1173.2020.14.COIMG-377

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