This is a PyTorch implementation of "SuperPoint: Self-Supervised Interest Point Detection and Description." Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich. ArXiv 2018. This code is partially based on the tensorflow implementation https://github.com/rpautrat/SuperPoint.
Please be generous to star this repo if it helps your research. This repo is a bi-product of our paper deepFEPE(IROS 2020).
- Descriptor loss: We tested descriptor loss using different methods, including dense method (as paper but slightly different) and sparse method. We notice sparse loss can converge more efficiently with similar performance. The default setting here is sparse method.
| Task | Homography estimation | Detector metric | Descriptor metric | ||||
|---|---|---|---|---|---|---|---|
| Epsilon = 1 | 3 | 5 | Repeatability | MLE | NN mAP | Matching Score | |
| Pretrained model | 0.44 | 0.77 | 0.83 | 0.606 | 1.14 | 0.81 | 0.55 |
| Sift (subpixel accuracy) | 0.63 | 0.76 | 0.79 | 0.51 | 1.16 | 0.70 | 0.27 |
| superpoint_coco_heat2_0_170k_hpatches_sub | 0.46 | 0.75 | 0.81 | 0.63 | 1.07 | 0.78 | 0.42 |
| superpoint_kitti_heat2_0_50k_hpatches_sub | 0.44 | 0.71 | 0.77 | 0.56 | 0.95 | 0.78 | 0.41 |
- Pretrained model is from SuperPointPretrainedNetwork.
- The evaluation is done under our evaluation scripts.
- COCO/ KITTI pretrained model is included in this repo.
- python == 3.6
- pytorch >= 1.1 (tested in 1.3.1)
- torchvision >= 0.3.0 (tested in 0.4.2)
- cuda (tested in cuda10)
conda create --name py36-sp python=3.6
conda activate py36-sp
pip install -r requirements.txt
pip install -r requirements_torch.txt # install pytorch
- paths for datasets ($DATA_DIR), logs are set in
setting.py
Datasets should be downloaded into $DATA_DIR. The Synthetic Shapes dataset will also be generated there. The folder structure should look like:
datasets/ ($DATA_DIR)
|-- COCO
| |-- train2014
| | |-- file1.jpg
| | `-- ...
| `-- val2014
| |-- file1.jpg
| `-- ...
`-- HPatches
| |-- i_ajuntament
| `-- ...
`-- synthetic_shapes # will be automatically created
`-- KITTI (accumulated folders from raw data)
| |-- 2011_09_26_drive_0020_sync
| | |-- image_00/
| | `-- ...
| |-- ...
| `-- 2011_09_28_drive_0001_sync
| | |-- image_00/
| | `-- ...
| |-- ...
| `-- 2011_09_29_drive_0004_sync
| | |-- image_00/
| | `-- ...
| |-- ...
| `-- 2011_09_30_drive_0016_sync
| | |-- image_00/
| | `-- ...
| |-- ...
| `-- 2011_10_03_drive_0027_sync
| | |-- image_00/
| | `-- ...
- MS-COCO 2014
- HPatches
- KITTI Odometry
- Notes:
- Start from any steps (1-4) by downloading some intermediate results
- Training usually takes 8-10 hours on one 'NVIDIA 2080Ti'.
- Currently Support training on 'COCO' dataset (original paper), 'KITTI' dataset.
- Tensorboard:
- log files is saved under 'runs/<\export_task>/...'
tensorboard --logdir=./runs/ [--host | static_ip_address] [--port | 6008]
python train4.py train_base configs/magicpoint_shapes_pair.yaml magicpoint_synth --eval
you don't need to download synthetic data. You will generate it when first running it.
Synthetic data is exported in ./datasets. You can change the setting in settings.py.
This is the step of homography adaptation(HA) to export pseudo ground truth for joint training.
- make sure the pretrained model in config file is correct
- make sure COCO dataset is in '$DATA_DIR' (defined in setting.py)
- config file:
export_folder: <'train' | 'val'> # set export for training or validation
python export.py <export task> <config file> <export folder> [--outputImg | output images for visualization (space inefficient)]
python export.py export_detector_homoAdapt configs/magicpoint_coco_export.yaml magicpoint_synth_homoAdapt_coco
- Edit 'export_folder' to 'val' in 'magicpoint_coco_export.yaml'
python export.py export_detector_homoAdapt configs/magicpoint_coco_export.yaml magicpoint_synth_homoAdapt_coco
- config
- check the 'root' in config file
- train/ val split_files are included in
datasets/kitti_split/.
python export.py export_detector_homoAdapt configs/magicpoint_kitti_export.yaml magicpoint_base_homoAdapt_kitti
You need pseudo ground truth labels to traing detectors. Labels can be exported from step 2) or downloaded from link. Then, as usual, you need to set config file before training.
- config file
- root: specify your labels root
- root_split_txt: where you put the train.txt/ val.txt split files (no need for COCO, needed for KITTI)
- labels: the exported labels from homography adaptation
- pretrained: specify the pretrained model (you can train from scratch)
- 'eval': turn on the evaluation during training
python train4.py <train task> <config file> <export folder> --eval
python train4.py train_joint configs/superpoint_coco_train_heatmap.yaml superpoint_coco --eval --debug
python train4.py train_joint configs/superpoint_kitti_train_heatmap.yaml superpoint_kitti --eval --debug
- set your batch size (originally 1)
- refer to: 'train_tutorial.md'
- Use pretrained model or specify your model in config file
./run_export.shwill run export then evaluation.
- download HPatches dataset (link above). Put in the $DATA_DIR.
python export.py <export task> <config file> <export folder> - Export keypoints, descriptors, matching
python export.py export_descriptor configs/magicpoint_repeatability_heatmap.yaml superpoint_hpatches_test
python evaluation.py <path to npz files> [-r, --repeatibility | -o, --outputImg | -homo, --homography ]
- Evaluate homography estimation/ repeatability/ matching scores ...
python evaluation.py logs/superpoint_hpatches_test/predictions --repeatibility --outputImg --homography --plotMatching
- Refer to another project: Feature-preserving image denoising with multiresolution filters
# export detection, description, matching
python export_classical.py export_descriptor configs/classical_descriptors.yaml sift_test --correspondence
# evaluate (use 'sift' flag)
python evaluation.py logs/sift_test/predictions --sift --repeatibility --homography - specify the pretrained model
- COCO dataset
logs/superpoint_coco_heat2_0/checkpoints/superPointNet_170000_checkpoint.pth.tar - KITTI dataset
logs/superpoint_kitti_heat2_0/checkpoints/superPointNet_50000_checkpoint.pth.tar
pretrained/superpoint_v1.pth
# show images saved in the folders
jupyter notebook
notebooks/visualize_hpatches.ipynb - 2020.08.05:
- Update pytorch nms from (eric-yyjau#19)
- Update and test KITTI dataloader and labels on google drive (should be able to fit the KITTI raw format)
- Update and test SIFT evaluate at step 5.
test step 5: evaluate on SIFT- Export COCO dataset in low resolution (240x320) instead of high resolution (480x640).
- Due to step 1 was done long time ago. We are still testing it again along with step 2-4. Please refer to our pretrained model or exported labels. Or let us know how the whole pipeline works.
- Warnings from tensorboard.
- Release notebooks with unit testing.
- Dataset: ApolloScape/ TUM.
Please cite the original paper.
@inproceedings{detone2018superpoint,
title={Superpoint: Self-supervised interest point detection and description},
author={DeTone, Daniel and Malisiewicz, Tomasz and Rabinovich, Andrew},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
pages={224--236},
year={2018}
}
Please also cite our DeepFEPE paper.
@misc{2020_jau_zhu_deepFEPE,
Author = {You-Yi Jau and Rui Zhu and Hao Su and Manmohan Chandraker},
Title = {Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints},
Year = {2020},
Eprint = {arXiv:2007.15122},
}
This implementation is developed by You-Yi Jau and Rui Zhu. Please contact You-Yi for any problems. Again the work is based on Tensorflow implementation by Rémi Pautrat and Paul-Edouard Sarlin and official SuperPointPretrainedNetwork. Thanks to Daniel DeTone for help during the implementation.
What have I learned from the implementation of deep learning paper?