Transformer

CAT-Net: Learning Canonical Appearance Transformations

CAT-Net

Code to accompany our paper “How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change”.

Dependencies

  • numpy
  • matpotlib
  • pytorch + torchvision (1.2)
  • Pillow
  • progress (for progress bars in train/val/test loops)
  • tensorboard + tensorboardX (for visualization)
  • pyslam + liegroups (optional, for running odometry/localization experiments)
  • OpenCV (optional, for running odometry/localization experiments)

Training the CAT

  1. Download the ETHL dataset from here or the Virtual KITTI dataset from here
    1. ETHL only: rename ethl1/2 to ethl1/2_static.
    2. ETHL only: Update the local paths in tools/make_ethl_real_sync.py and run python3 tools/make_ethl_real_sync.py to generate a synchronized copy of the real sequences.
  2. Update the local paths in run_cat_ethl/vkitti.py and run python3 run_cat_ethl/vkitti.py to start training.
  3. In another terminal run tensorboard --port [port]

     

     

     

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