This is an code of the algorithm described in "MegaDepth: Learning Single-View Depth Prediction from Internet Photos, Z. Li and N. Snavely, CVPR 2018". The code skeleton is based on "https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix". If you use our code or models for academic purposes, please consider citing:

@inproceedings{MDLi18,
  	title={MegaDepth: Learning Single-View Depth Prediction from Internet Photos},
  	author={Zhengqi Li and Noah Snavely},
  	booktitle={Computer Vision and Pattern Recognition (CVPR)},
  	year={2018}
}

• The code was written in Pytorch and Python 2.7, but it should be easy to adapt it to Python 3 version if needed.
• You might need skimage, h5py libraries installed for python before running the code.

• Download MegaDepth V1 dataset from project website: http://www.cs.cornell.edu/projects/megadepth/.
• Download pretrained model (specific for MD dataset) from http://www.cs.cornell.edu/projects/megadepth/dataset/models/best_vanila_net_G.pth and put it in "checkpoints/test_local/best_vanila_net_G.pth"
• Download test list files from http://www.cs.cornell.edu/projects/megadepth/dataset/data_lists/test_lists.tar.gz, it should include two folders corresponding to images with landscape and portrait orientations.
• To compute scale invarance RMSE on MD testset, change the variable "dataset_root" in python file "rmse_error_main.py" to the root directory of MegaDepth_v1 folder, and change variable "test_list_dir_l" and "test_list_dir_p" to corresponding folder paths of test lists, and run:

    python rmse_error_main.py

• To compute Structure from Motion Disagreement Rate (SDR), change the variable "dataset_root" in python file "rmse_error_main.py" to the root directory of MegaDepth_v1 folder, and change variable "test_list_dir_l" and "test_list_dir_p" to corresponding folder paths of test lists, and run:

    python SDR_compute.py

• If you want to run our model on arbitrary Internet photos, please download pretrained model from http://www.cs.cornell.edu/projects/megadepth/dataset/models/best_generalization_net_G.pth, which has much better generalization ability (qualitatively speaking) to completely unknown scenes.