Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection

TL;DR

This paper introduces Hformer, a hybrid CNN-Transformer model for phase unwrapping in fringe projection 3D measurement, demonstrating improved fringe order prediction over CNN-only models.

Contribution

The paper pioneers integrating Transformer architecture into phase unwrapping, creating a hybrid model that outperforms CNN-based methods like U-Net and DCNN.

Findings

Hformer achieves better fringe order prediction accuracy.

Ablation studies confirm the effectiveness of feature pyramid networks and flipping strategies.

The approach offers a new direction for deep learning in phase unwrapping.

Abstract

Recently, deep learning has attracted more and more attention in phase unwrapping of fringe projection three-dimensional (3D) measurement, with the aim to improve the performance leveraging the powerful Convolutional Neural Network (CNN) models. In this paper, for the first time (to the best of our knowledge), we introduce the Transformer into the phase unwrapping which is different from CNN and propose Hformer model dedicated to phase unwrapping via fringe order prediction. The proposed model has a hybrid CNN-Transformer architecture that is mainly composed of backbone, encoder and decoder to take advantage of both CNN and Transformer. Encoder and decoder with cross attention are designed for the fringe order prediction. Experimental results show that the proposed Hformer model achieves better performance in fringe order prediction compared with the CNN models such as U-Net and DCNN. Moreover, ablation study on Hformer is made to verify the improved feature pyramid networks (FPN) and testing strategy with flipping in the predicted fringe order. Our work opens an alternative way to deep learning based phase unwrapping methods, which are dominated by CNN in fringe projection 3D measurement.