TPDNet: Texture-Guided Phase-to-DEPTH Networks to Repair Shadow-Induced Errors for Fringe Projection Profilometry

This paper proposes a phase-to-depth deep learning model to repair shadow-induced errors for fringe projection profilometry (FPP). The model comprises two hourglass branches that extract information from texture images and phase maps and fuses the information from the two branches by concatenation a...

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Auteurs principaux:	Jiaqiong Li, Beiwen Li
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2023-02-01
Collection:	Photonics
Sujets:	fringe projection profilometry shadow-induced errors texture-guided phase-to-depth networks
Accès en ligne:	https://www.mdpi.com/2304-6732/10/3/246
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