Implementation of Attention Weighted Local Descriptors (TPAMI2023).
Unofficial Pytorch implementation of SuperPoint.
To do：
- Evaluation code and Trained model for AWDesc
- Training code and a more detailed readme (Coming soon)
- Training code of SuperPoint
pip install -r requirement.txt,
HPatches Image Matching Benchmark
1.Download the trained model:
AWDesc_CA:
https://drive.google.com/file/d/1qrvdd3KVYFl6EwH8s5IS5p_Hs26xIKRD/view?usp=sharing
AWDesc_Tiny:
https://drive.google.com/drive/folders/1PGHiGojkE7qCp1T-l9JSn4aJ7gN0_ua6?usp=sharing
and place it in the "ckpt/mtldesc".
2.Download the HPatches dataset：
cd evaluation_hpatch/hpatches_sequences
bash download.sh
3.Extract local descriptors：
cd evaluation_hpatch
CUDA_VISIBLE_DEVICES=0 python export.py --tag [Descriptor_suffix_name] --top-k 10000 --output_root [out_dir] --config ../configs/MTLDesc_eva.yaml
4.Evaluation
cd evaluation_hpatch/hpatches_sequences
jupyter-notebook
run HPatches-Sequences-Matching-Benchmark.ipynb
AWDesc-CA
Download dataset: https://drive.google.com/file/d/1Uz0hVFPxWsE71V77kXZ973iY2GuXC20b/view?usp=sharing
Set the dataset path in the configuration file configs/AWDesc_train.yaml
mega_image_dir: /data/Mega_train/image #images
mega_keypoint_dir: /data/Mega_train/keypoint #keypoints
mega_despoint_dir: /data/Mega_train/despoint #descriptor correspondence points
python train.py --gpus 0 --configs configs/AWDesc_train.yaml --indicator awdesc_ca
AWDesc-Tiny
Download dataset: https://pan.baidu.com/s/1-1rpNxYsNl5fVRKB6EWo4A?pwd=elcb
download code：elcb