Single-input dual-output 3D shape reconstruction

This dataset is used to train the SIDO network which converts a single structured-light (fringe) image to two intermediate outputs before subsequent 3D shape reconstruction. If you use the datasets for your research, please consider citing our related publications: 1) H. Nguyen, Y. Wang, and Z. Wang, "Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks," Sensors 20, 3718, 2020. 2) H. Nguyen and Z. Wang, "Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network," Photonics 8, 459, 2021. 3) H. Nguyen, E. Novak, and Z. Wang, "Accurate 3D reconstruction via fringe-to-phase network," Measurement 190, 110663, 2022. 4) AH. Nguyen, K. Ly, C. Li, and Z. Wang, "Single-shot 3D shape acquisition using a learning-based structured light technique," Appl. Opt. 61, 8589-8599, 2022. 5) AH. Nguyen, O. Rees, and Z. Wang, "Learning-based 3D imaging from single structured-light image," Graph. Models 126, 101171, 2023.本数据集用于训练SIDO网络，该网络可将单幅结构光（条纹）图像转换为两类中间输出，进而支撑后续的三维形状重建任务。若您将本数据集应用于学术研究，请引用以下相关文献： 1) H. Nguyen、Y. Wang与Z. Wang，《基于结构光与深度卷积神经网络的单幅三维形状重建》，*Sensors*，2020年，第20卷，第3718页。 2) H. Nguyen与Z. Wang，《基于条纹到条纹网络的单幅结构光图像高精度三维形状重建》，*Photonics*，2021年，第8卷，第459页。 3) H. Nguyen、E. Novak与Z. Wang，《基于条纹到相位网络的高精度三维重建》，*Measurement*，2022年，第190卷，第110663页。 4) AH. Nguyen、K. Ly、C. Li与Z. Wang，《基于学习型结构光技术的单幅三维形状采集》，*Appl. Opt.*，2022年，第61卷，第8589-8599页。 5) AH. Nguyen、O. Rees与Z. Wang，《基于单幅结构光图像的学习型三维成像》，*Graph. Models*，2023年，第126卷，第101171页。