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.