This code is based on the Generative Inpainting CVPR 2018 paper and it's repository.

Based on the Generative Inpainting network we proposed adaptations to deal with disparity images inpainting. Our results have been publish on the IV 2019 and on this repository we have the code used on this publication.

To use this code, please follow the installation instructions from the original repository since de dependencies are the same.

The directory /training_data has the disparity images for the training and the /data_flist contains the .flist files wich list the images.

For training the command is:

python3 train.py

For testing use:

python3 test.py --image 'input_image' --mask 'removed_area_mask' --output 'output_image' --checkpoint_dir model_logs/'trained_model_dir'

Depth map object removal example and the respective 3D mesh reconstruction: