News

• The ALIKED is released.
• The ALIKE training code is released.

ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

ALIKE applies a differentiable keypoint detection module to detect accurate sub-pixel keypoints. The network can run at 95 frames per second for 640 x 480 images on NVIDIA Titan X (Pascal) GPU and achieve equivalent performance with the state-of-the-arts. ALIKE benefits real-time applications in resource-limited platforms/devices. Technical details are described in this paper.

Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter C. Y. Chen, Zhengguo Li, "ALIKE: Accurate and Lightweight Keypoint
Detection and Descriptor Extraction," IEEE Transactions on Multimedia, 2022.

If you use ALIKE in an 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},
}

1. Prerequisites

The required packages are listed in the requirements.txt :

pip install -r requirements.txt

2. Models

The off-the-shelf weights of four variant ALIKE models are provided in models/ .

3. Run demo

$ python demo.py -h
usage: demo.py [-h] [--model {alike-t,alike-s,alike-n,alike-l}]
               [--device DEVICE] [--top_k TOP_K] [--scores_th SCORES_TH]
               [--n_limit N_LIMIT] [--no_display] [--no_sub_pixel]
               input

ALike Demo.

positional arguments:
  input                 Image directory or movie file or "camera0" (for
                        webcam0).

optional arguments:
  -h, --help            show this help message and exit
  --model {alike-t,alike-s,alike-n,alike-l}
                        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).
  --no_sub_pixel        Do not detect sub-pixel keypoints (default: False).

4. Examples

KITTI example

python demo.py assets/kitti 

TUM example

python demo.py assets/tum 

5. Efficiency and performance

Models	Parameters	GFLOPs(640x480)	MHA@3 on Hpatches	mAA(10°) on IMW2020-test (Stereo)
D2-Net(MS)	7653KB	889.40	38.33%	12.27%
LF-Net(MS)	2642KB	24.37	57.78%	23.44%
SuperPoint	1301KB	26.11	70.19%	28.97%
R2D2(MS)	484KB	464.55	71.48%	39.02%
ASLFeat(MS)	823KB	77.58	73.52%	33.65%
DISK	1092KB	98.97	70.56%	51.22%
ALike-N	318KB	7.909	75.74%	47.18%
ALike-L	653KB	19.685	76.85%	49.58%

Evaluation on Hpatches

• Download hpatches-sequences-release and put it into hseq/hpatches-sequences-release.
• Remove the unreliable sequences as D2-Net.
• Run the following command to evaluate the performance:

  python hseq/eval.py

For more details, please refer to the paper.