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# 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 RTX 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{Zhao2022ALIKE,
title={ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction},
author={Xiaoming Zhao and Xingming Wu and Jinyu Miao and Weihai Chen and Peter C. Y. Chen and Zhengguo Li},
journal={IEEE Transactions on Multimedia},
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 unrelaiable 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). |