This repository is an implementation of paper "OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation".

Peng Li, Jiayin Zhao, Jingyao Wu, Chao Deng, Yuqi Han, Haoqian Wang, Tao Yu

Tsinghua University

• Set the hyper-parameters in option.py if needed. We have provided our default settings in the realeased codes.
• Run train.py to perform network training.
• Checkpoint will be saved to ./checkpoints/.
• If you want to train the network with the HCI dataset, place the input LFs into /dataset/hci_dataset.

• Place the input LFs into ./dataset (see the attached example).
• Run test.py to perform inference on each test scene.
• The result files (i.e., general_52_eslf_depth.tif) will be saved to ./results/OPENet/latest/scan_LF/.