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Optimized loss function in deep learning profilometry for improved prediction performance
Author(s) -
Sam Van der Jeught,
Pieter G.G. Muyshondt,
Iván Lobato
Publication year - 2021
Publication title -
jphys photonics
Language(s) - English
Resource type - Journals
ISSN - 2515-7647
DOI - 10.1088/2515-7647/abf030
Subject(s) - profilometer , deep learning , computer science , artificial intelligence , artificial neural network , function (biology) , range (aeronautics) , pattern recognition (psychology) , computer vision , surface finish , engineering , mechanical engineering , evolutionary biology , biology , aerospace engineering
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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