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| # News | |
| - The [ALIKED](https://github.com/Shiaoming/ALIKED) is released. | |
| - The [ALIKE training code](https://github.com/Shiaoming/ALIKE/raw/main/assets/ALIKE_code.zip) 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](https://arxiv.org/pdf/2112.02906.pdf). | |
| > ``` | |
| > 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` : | |
| ```shell | |
| pip install -r requirements.txt | |
| ``` | |
| ## 2. Models | |
| The off-the-shelf weights of four variant ALIKE models are provided in `models/` . | |
| ## 3. Run demo | |
| ```shell | |
| $ 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 | |
| ```shell | |
| python demo.py assets/kitti | |
| ``` | |
|  | |
| ### TUM example | |
| ```shell | |
| python demo.py assets/tum | |
| ``` | |
|  | |
| ## 5. Efficiency and performance | |
| | Models | Parameters | GFLOPs(640x480) | MHA@3 on Hpatches | mAA(10°) on [IMW2020-test](https://www.cs.ubc.ca/research/image-matching-challenge/2021/leaderboard) (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](https://hpatches.github.io/) and put it into `hseq/hpatches-sequences-release`. | |
| - Remove the unreliable sequences as D2-Net. | |
| - Run the following command to evaluate the performance: | |
| ```shell | |
| python hseq/eval.py | |
| ``` | |
| For more details, please refer to the [paper](https://arxiv.org/abs/2112.02906). | |