Enhanced fringe-to-phase framework using deep learning

TL;DR

This paper introduces SFNet, a deep learning framework that accurately reconstructs 3D shapes from just two fringe images by transforming them into an absolute phase, improving robustness and accuracy in structured light imaging.

Contribution

The paper presents a novel symmetric fusion network that predicts absolute phase from two fringe images, incorporating multi-frequency information for enhanced reliability.

Findings

High accuracy with only two fringe images

Effective phase prediction validated by experiments

Framework outperforms traditional methods

Abstract

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.