Awesome
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 and network_v1, the corresponding pre-trained models are in 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.
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}
}