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TRAINING.md

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Traininig LoFTR

Dataset setup

Generally, two parts of data are needed for training LoFTR, the original dataset, i.e., ScanNet and MegaDepth, and the offline generated dataset indices. The dataset indices store scenes, image pairs, and other metadata within each dataset used for training/validation/testing. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files. However, the relative poses of ScanNet image pairs are not stored due to the enormous resulting file size.

Download datasets

MegaDepth

We use depth maps provided in the original MegaDepth dataset as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by D2-Net. You can download them separately from the following links.

  • MegaDepth undistorted images and processed depths
    • Note that we only use depth maps.
    • Path of the download data will be referreed to as /path/to/megadepth
  • D2-Net preprocessed images
    • Images are undistorted manually in D2-Net since the undistorted images from MegaDepth do not come with corresponding intrinsics.
    • Path of the download data will be referreed to as /path/to/megadepth_d2net

ScanNet

Please set up the ScanNet dataset following the official guide

NOTE: We use the python exported data, instead of the c++ exported one.

Download the dataset indices

You can download the required dataset indices from the following link. After downloading, unzip the required files.

unzip downloaded-file.zip

# extract dataset indices
tar xf train-data/megadepth_indices.tar
tar xf train-data/scannet_indices.tar

# extract testing data (optional)
tar xf testdata/megadepth_test_1500.tar
tar xf testdata/scannet_test_1500.tar

Build the dataset symlinks

We symlink the datasets to the data directory under the main LoFTR project directory.

# scannet
# -- # train and test dataset
ln -s /path/to/scannet_train/* /path/to/LoFTR/data/scannet/train
ln -s /path/to/scannet_test/* /path/to/LoFTR/data/scannet/test
# -- # dataset indices
ln -s /path/to/scannet_indices/* /path/to/LoFTR/data/scannet/index

# megadepth
# -- # train and test dataset (train and test share the same dataset)
ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/LoFTR/data/megadepth/train
ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/LoFTR/data/megadepth/test
# -- # dataset indices
ln -s /path/to/megadepth_indices/* /path/to/LoFTR/data/megadepth/index

Training

We provide training scripts of ScanNet and MegaDepth. The results in the LoFTR paper can be reproduced with 32/64 GPUs with at least 11GB of RAM for ScanNet, and 8/16 GPUs with at least 24GB of RAM for MegaDepth. For a different setup (e.g., training with 4 gpus on ScanNet), we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed.

Training scripts of the optimal-transport matcher end with "_ot" and ones of the dual-softmax matcher end with "_ds".

The released training scripts use smaller setups comparing to ones used for training the released models. You could manually scale the setup (e.g., using 32 gpus instead of 4) to reproduce our results.

Training on ScanNet

scripts/reproduce_train/indoor_ds.sh

NOTE: It uses 4 gpus only. Reproduction of paper results is not guaranteed under this setup.

Training on MegaDepth

scripts/reproduce_train/outdoor_ds.sh

NOTE: It uses 4 gpus only, with smaller image sizes of 640x640. Reproduction of paper results is not guaranteed under this setup.

Updated Training Strategy

In the released training code, we use a slightly modified version of the coarse-level training supervision comparing to the one described in our paper. For example, as described in our paper, we only supervise the ground-truth positive matches when training the dual-softmax model. However, the entire confidence matrix produced by the dual-softmax matcher is supervised by default in the released code, regardless of the use of softmax operators. This implementation is counter-intuitive and unusual but leads to better evaluation results on estimating relative camera poses. The same phenomenon applies to the optimal-transport matcher version as well. Note that we don't supervise the dustbin rows and columns under the dense supervision setup.

NOTE: To use the sparse supervision described in our paper, set _CN.LOFTR.MATCH_COARSE.SPARSE_SPVS = False.