CVFT + LPN

Experiment Dataset

We use two existing dataset to do the experiments

• CVUSA datset: a dataset in America, with pairs of ground-level images and satellite images. All ground-level images are panoramic images.
  The dataset can be accessed from https://github.com/viibridges/crossnet

• CVACT dataset: a dataset in Australia, with pairs of ground-level images and satellite images. All ground-level images are panoramic images.
  The dataset can be accessed from https://github.com/Liumouliu/OriCNN

Dataset Preparation

Please Download the two datasets from above links, and then put them under the director "Data/". The structure of the director "Data/" should be: "Data/CVUSA/ Data/ANU_data_small/"

Models:

There is also an "Initialize" model for your own training step. The VGG16 part in the "Initialize_model" model is initialised by the online model and other parts are initialised randomly.

Please put them under the director of "Model/" and then you can use them for training or evaluation.

Codes

1. Training:

   CVUSA: python train_cvusa_lpn.py --multi_loss

   CVACT: python train_cvact_lpn.py --multi_loss

2. Evaluation:

   CVUSA: python test_cvusa.py --multi_loss

   CVACT: python test_cvact.py --multi_loss

Reference

Optimal Feature Transport for Cross-View Image Geo-Localization

github