ALIKED is an improvement on ALIKE, which introduces the Sparse Deformable Descriptor Head (SDDH) to efficiently extract deformable descriptors. Compared with ALIKE, ALIKED can extract more robust descriptors in a more efficient way. The technical details are described in this paper.
Xiaoming Zhao, Xingming Wu, Weihai Chen, Peter C.Y. Chen, Qingsong Xu, and Zhengguo Li, "ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation", IEEE Transactions on Instrumentation & Measurement, 2023.
If you use ALIKED in academic work, please cite:
@article{Zhao2023ALIKED,
title = {ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation},
url = {https://arxiv.org/pdf/2304.03608.pdf},
doi = {10.1109/TIM.2023.3271000},
journal = {IEEE Transactions on Instrumentation & Measurement},
author = {Zhao, Xiaoming and Wu, Xingming and Chen, Weihai and Chen, Peter C. Y. and Xu, Qingsong and Li, Zhengguo},
year = {2023},
volume = {72},
pages = {1-16},
}
@article{Zhao2022ALIKE,
title = {ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction},
url = {http://arxiv.org/abs/2112.02906},
doi = {10.1109/TMM.2022.3155927},
journal = {IEEE Transactions on Multimedia},
author = {Zhao, Xiaoming and Wu, Xingming and Miao, Jinyu and Chen, Weihai and Chen, Peter C. Y. and Li, Zhengguo},
month = march,
year = {2022},
}
The required packages are listed in the requirements.txt :
pip install -r requirements.txtBuild custom_ops:
cd custom_ops
sh build.shThe pretrained ALIKED models are provided in models/ .
Example:
python demo_pair.py assets/st_pauls_cathedral 
python demo_pair.py assets/piazza_san_marco
Usage:
$ python demo_pair.py -h
usage: demo_pair.py [-h] [--model {aliked-t16,aliked-n16,aliked-n16rot,aliked-n32}]
[--device DEVICE] [--top_k TOP_K] [--scores_th SCORES_TH]
[--n_limit N_LIMIT] input
ALIKED image pair Demo.
positional arguments:
input Image directory.
options:
-h, --help show this help message and exit
--model {aliked-t16,aliked-n16,aliked-n16rot,aliked-n32}
The model configuration
--device DEVICE Running device (default: cuda).
--top_k TOP_K Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)
--scores_th SCORES_TH
Detector score threshold (default: 0.2).
--n_limit N_LIMIT Maximum number of keypoints to be detected (default: 5000).Example:
python demo_seq.py assets/tum 
Usage:
$ python demo_seq.py -h
usage: demo_seq.py [-h] [--model {aliked-t16,aliked-n16,aliked-n16rot,aliked-n32}]
[--device DEVICE] [--top_k TOP_K] [--scores_th SCORES_TH]
[--n_limit N_LIMIT] [--no_display] input
ALIKED sequence Demo.
positional arguments:
input Image directory or movie file or "camera0" (for webcam0).
options:
-h, --help show this help message and exit
--model {aliked-t16,aliked-n16,aliked-n16rot,aliked-n32}
The model configuration
--device DEVICE Running device (default: cuda).
--top_k TOP_K Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)
--scores_th SCORES_TH
Detector score threshold (default: 0.2).
--n_limit N_LIMIT Maximum number of keypoints to be detected (default: 5000).
--no_display Do not display images to screen. Useful if running remotely (default: False).
- The
aliked-n16rotis trained with rotation augmentation, it can handle some image rotation and is more stable on viewpoint change. But it can still have difficulty on large image rotations. - ALIKED is detector-based, and it is designed as light-weight as possible, so it is not comparable with the detector-free methods.
- The training code: I am sorry that I am not allowed to publish the training code of ALIKED, you can refer to the training code of ALIKE.
For more details, please refer to the paper.
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