Enhanced fringe-to-phase framework using deep learning
Won-Hoe Kim Bongjoong Kim Hyung-Gun Chi Jae-Sang Hyun
In Fringe Projection Profilometry (FPP), achieving robust and accurate 3D
reconstruction with a limited number of fringe patterns remains a challenge in
structured light 3D imaging. Conventional methods require a set of fringe
images, but using only one or two patterns complicates phase recovery and
unwrapping. In this study, we introduce SFNet, a symmetric fusion network that
transforms two fringe images into an absolute phase. To enhance output
reliability, Our framework predicts refined phases by incorporating information
from fringe images of a different frequency than those used as input. This
allows us to achieve high accuracy with just two images. Comparative
experiments and ablation studies validate the effectiveness of our proposed
method. The dataset and code are publicly accessible on our project page
https://wonhoe-kim.github.io/SFNet.