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Title
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Optimized loss function in deep learning profilometry for improved prediction performance
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Author
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Abstract
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| Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions. |
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Language
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English
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Source (journal)
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JPhys Photonics / Institute of Physics
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Publication
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IOP Publishing
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2021
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ISSN
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2515-7647
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DOI
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10.1088/2515-7647/ABF030
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Volume/pages
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3
:2
(2021)
, 10 p.
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Article Reference
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024014
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ISI
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000641030000001
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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