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Author van der Jeught, S.; Muyshondt, P.G.G.; Lobato, I. url  doi
openurl 
  Title Optimized loss function in deep learning profilometry for improved prediction performance Type A1 Journal article
  Year (down) 2021 Publication JPhys Photonics Abbreviated Journal  
  Volume 3 Issue 2 Pages 024014  
  Keywords A1 Journal article; Electron microscopy for materials research (EMAT)  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000641030000001 Publication Date 2021-03-18  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2515-7647 ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor Times cited Open Access OpenAccess  
  Notes Approved Most recent IF: NA  
  Call Number UA @ admin @ c:irua:178171 Serial 6797  
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