Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

This code was developed and tested with python 3.6, Pytorch 1.8.0, and CUDA 10.2 on Ubuntu 18.04. It is based on Eduardo Romera's ERFNet implementation (PyTorch Version).

Please install the required packages:

pip install -r requirement.txt

Please download them from Part1, Part2, Part3 and Part4.

Please download it from here

Training the DL-SPU model from scratch by running

python train/train.py

Note: Please prepare the training datset before trainging.

Our pretrained model is in the file of "model_best.pth".

@article{Luo2022,
archivePrefix = {arXiv},
arxivId = {2208.03524},
author = {Luo, Xiaolong and Song, Wanzhong and Bai, Songlin and Li, Yu and Zhao, Zhihe},
eprint = {2208.03524},
title = {{Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement}},
url = {http://arxiv.org/abs/2208.03524},
year = {2022}
}

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/