Spaces:
Running
Running
File size: 5,491 Bytes
a80d6bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
# 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).
|