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. <br> If you use the datasets for your research, please consider citing our related publications: <br> 1) H. Nguyen, Y. Wang, and Z. Wang, "Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks," <em>Sensors</em> <strong>20</strong>, 3718, 2020. 2) H. Nguyen and Z. Wang, "Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network," <em>Photonics</em> <strong>8</strong>, 459, 2021. 3) H. Nguyen, E. Novak, and Z. Wang, "Accurate 3D reconstruction via fringe-to-phase network," <em>Measurement</em> <strong>190</strong>, 110663, 2022. 4) AH. Nguyen, K. Ly, C. Li, and Z. Wang, "Single-shot 3D shape acquisition using a learning-based structured light technique," <em>Appl. Opt.</em> <strong>61</strong>, 8589-8599, 2022. 5) AH. Nguyen, O. Rees, and Z. Wang, "Learning-based 3D imaging from single structured-light image," <em>Graph. Models</em> <strong>126</strong>, 101171, 2023.