| # Rethinking Low-level Features for Interest Point Detection and Description | |
| ## Dependency | |
| - pytorch | |
| - torchvision | |
| - cv2 | |
| - tqdm | |
| We use cuda 11.4/python 3.8.13/torch 1.10.0/torchvision 0.11.0/opencv 3.4.8 for training and testing. | |
| ## Pre-trained models | |
| We provide two versions of LANet with different structure in [network_v0](network_v0) and [network_v1](network_v1), the corresponding pre-trained models are in [checkpoints](checkpoints). | |
| - v0: The original version used in our paper. | |
| - v1: An improved version that has a better over all performance. | |
| ## Training | |
| Download the COCO dataset: | |
| ``` | |
| cd datasets/COCO/ | |
| wget http://images.cocodataset.org/zips/train2017.zip | |
| unzip train2017.zip | |
| ``` | |
| Prepare the training file: | |
| ``` | |
| python datasets/prepare_coco.py --raw_dir datasets/COCO/train2017/ --saved_dir datasets/COCO/ | |
| ``` | |
| To train the model (v0) on COCO dataset, run: | |
| ``` | |
| python main.py --train_root datasets/COCO/train2017/ --train_txt datasets/COCO/train2017.txt | |
| ``` | |
| ## Evaluation | |
| ### Evaluation on HPatches dataset | |
| Download the HPatches dataset: | |
| ``` | |
| cd datasets/HPatches/ | |
| wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz | |
| tar -xvf hpatches-sequences-release.tar.gz | |
| ``` | |
| To evaluate the pre-trained model, run: | |
| ``` | |
| python test.py --test_dir ./datasets/HPatches/hpatches-sequences-release | |
| ``` | |
| ## License | |
| The code is released under the [MIT license](LICENSE). | |
| ## Citation | |
| Please use the following citation when referencing our work: | |
| ``` | |
| @InProceedings{Wang_2022_ACCV, | |
| author = {Changhao Wang and Guanwen Zhang and Zhengyun Cheng and Wei Zhou}, | |
| title = {Rethinking Low-level Features for Interest Point Detection and Description}, | |
| booktitle = {Computer Vision - {ACCV} 2022 - 16th Asian Conference on Computer | |
| Vision, Macao, China, December 4-8, 2022, Proceedings, Part {II}}, | |
| series = {Lecture Notes in Computer Science}, | |
| volume = {13842}, | |
| pages = {108--123}, | |
| year = {2022} | |
| } | |
| ``` | |
| ## Related Projects | |
| https://github.com/TRI-ML/KP2D | |