Awesome

TFMatch: Learning-based image matching in TensorFlow

About

TFMatch provides a code base that tackles learning-based image matching problems, including local feature detectors and local feature descriptors. Research works below are implemented in this project:

Usage

All above works use the same training data from GL3D. It is recommended to use this project for model training, then use the scripts provided in the respective project for model evaluation.

Data preparation

To prepare the training data, please refer to the instructions in GL3D to download necessary geometric labels, including:

# undistorted images (62G)
bash download_data.sh gl3d_imgs 0 125
# camera parameters (<0.1G)
bash download_data.sh gl3d_cams 0 0
# SIFT keypoints (28G)
bash download_data.sh gl3d_kpts 0 57
# SIFT correspondences (6.1G)
bash download_data.sh gl3d_corr 0 12
# depth maps from MVS (30G)
bash download_data.sh gl3d_depths 0 59

To enrich the data diversity, you may also download phototourism data. You may alternatively use the blended images and rendered depths for training ASLFeat, which provides better geometric alignment, to do so:

# blended images (58G)
bash download_data.sh gl3d_blended_images 0 117
# rendered depth maps (30G)
bash download_data.sh gl3d_rendered_depths 0 59

Data parsing

After unzipping the downloaded data, you are ready to train GeoDesc and ASLFeat. Please be noded that, for the first time you run into any training, the program will need to parse the dataset and generate a set of intermediate data samples, which we refer to as match sets.

Concretely, it will create a folder named match_set in each project folder, e.g., GL3D/data/000000000000000000000000/match_sets, with a set of *.match_set files that specifies the indexes of an image pair, the correspondences (for GeoDesc and ContextDesc), camera intrinsics and relative pose (for ASLFeat). Be noted that the data preparation may take around 2.5 hours.

After then, a lock file will be generated in the project folder (i.e., .complete), and later training will skip this step as long as the lock file is found. To regenerate the match sets, pass --regenerate when calling the training script.

The data sampling is also conducted when generating match sets, by checking such as the number of correspondences or the rotation difference between cameras. Please refer to preprocess.py and tools/io.py for details.

Training GeoDesc

To train GeoDesc, you may checkout configs/train_geodesc_config.yaml, set up the data root, model root and query GPU index in train_geodesc.sh, then call:

sh train_geodesc.sh

The model will be saved in ckpt-geodesc.

Training ASLFeat

As described in the paper, we conduct a two-stage training for ASLFeat. First, you may configure configs/train_aslfeat_base_config.yaml and train_aslfeat_base.sh, and obtain the base model of ASLFeat without DCNs by calling:

sh train_aslfeat_base.sh

The model will be saved in ckpt-aslfeat-base. Next, you may configure configs/train_aslfeat_dcn_config.yaml and train_aslfeat_dcn.sh, and obtain the final model of ASLFeat with DCNs by calling:

sh train_aslfeat_dcn.sh

The model will be saved in ckpt-aslfeat-dcn. To use blended images and rendered depths, pass --data_split blendedmvg when calling the training script.

Training ContextDesc

To train ContextDesc, you will need to additionally prepare the regional features. Limited by the storage space, we do not host those feature files on AWS. You may use the retrieval model as provided in ContextDesc project, extract and save the regional features as binary files regarding feature list.

More concretely, you will need to resize each input image to 448x448, and save the feature file by reg_feat.astype(np.float32).tofile(save_path), where reg_feat is a 14x14x2048 numpy array (TODO: provide a script to extract the features).

Once the regional features are extracted, you may checkout config/train_contextdesc_config.yaml and train_contextdesc.sh, then call:

sh train_contextdesc.sh

The model will be saved in ckpt-contextdesc.

Post-conference update

After serveral iterations, this project may not produce the exact results as reported in the respective papers, but overall performs better. Here we list the major changes in the implementation and the insight behind.

Future Support

I will have limited access to maintain this project as I am about to graduate. If you encounter any problem, feel free to open an issue or PR, and I may have other labmates to help to review.