init fusion lcd orin config

2026-03-04 20:07:57 +08:00
commit bc0498e453
42 changed files with 4750 additions and 0 deletions
--- a/ALIKE/README.md
+++ b/ALIKE/README.md
@@ -0,0 +1,117 @@
+# News: the cpp version is released [ALIKE-cpp](https://github.com/Shiaoming/ALIKE-cpp).
+
+# 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.
+> ```
+
+![](./assets/alike.png)
+
+
+If you use ALIKE in an academic work, please cite:
+
+```
+@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 
+```
+![](./assets/kitti.gif)
+
+### TUM example
+```shell
+python demo.py assets/tum 
+```
+![](./assets/tum.gif)
+
+## 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).