Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/vcg-uvic/lf-net-release
Code Release for LF-Net: Learning Local Features from Images
https://github.com/vcg-uvic/lf-net-release
Last synced: 5 days ago
JSON representation
Code Release for LF-Net: Learning Local Features from Images
- Host: GitHub
- URL: https://github.com/vcg-uvic/lf-net-release
- Owner: vcg-uvic
- License: other
- Created: 2018-07-27T09:24:32.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-21T21:10:46.000Z (almost 2 years ago)
- Last Synced: 2024-08-03T01:15:13.774Z (3 months ago)
- Language: Python
- Homepage:
- Size: 3.79 MB
- Stars: 311
- Watchers: 19
- Forks: 66
- Open Issues: 14
-
Metadata Files:
- Readme: readme.md
- License: license.md
Awesome Lists containing this project
- awesome-image-alignment-and-stitching - **2018NeurIPS**
README
# LF-Net: Learning Local Features from Images
This repository is a tensorflow implementation for Y. Ono, E. Trulls, P. Fua,
K.M. Yi, "LF-Net: Learning Local Features from Images". If you use this code in
your research, please cite [the paper](https://arxiv.org/abs/1805.09662).
## Important Note regarding the use of ratio tests
Do **NOT** use the ratio test for descriptor matching! The commonly-used ratio
test depends on the distribution of descriptor distances, and the threshold
differs from one descriptor to another. Commonly used thresholds (0.9 0.7) are
actually harmful for LF-Net. If you want to use the ratio test, you need to
either tune this manually, or use statistical analysis as Lowe did for SIFT.
## Installation
This code is based on Python3.6.5 and tensorflow with CUDA-8.0. For more details on
the required libraries, see `requirements.txt`. You can also easily prepare
this by doing
```
pip install -r requirements.txt
```
## Docker image
We created a self-contained [Docker image](https://hub.docker.com/r/jiangweivcg/lf-net-release-env), for running the keypoint extraction demo easily. Make sure you have the nvidia docker runtime.
To launch a container:
`docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -ti --name lf-net-container -v /path/to/code_repo:/home jiangweivcg/lf-net-release-env`
To run the ` run_lfnet.py` script inside the container:
`cd /home`
`python run_lfnet.py --in_dir=/path/to/images --out_dir=/path/to/outputs`
## Pretrained models and example dataset
Download the [pretrained
models](https://www.cs.ubc.ca/research/kmyi_data/files/2018/lf-net/pretrained.tar.gz) and
the [scare_coeur
sequence](https://www.cs.ubc.ca/research/kmyi_data/files/2018/lf-net/sacre_coeur.tar.gz). Extract
them to the current folder so that they fall under `release/models/outdoor` for
example.
For other datasets, we do not plan to release them at the moment. Please do not
contact us for explanations on the training phase. We are providing them **as
is** as a reference implementation.
## Updates new pretrained models
Download [pretrained model without rotation augmentation ](https://www.cs.ubc.ca/research/kmyi_data/files/2018/lf-net/lfnet-norotaug.tar.gz)
### Updates since the arXiv version
The provided pre-trained models are trained with full 360 degree augmentation
for orientation. Thus, the results you get from these models are slightly
different from the one reported in arXiv. We have further included a
consistency term on the orientation assignment.
## Running the keypoint extraction demo
To run LF-Net for all images in a given directory, simply type:
```
python run_lfnet.py --in_dir=images --out_dir=outputs
```
In addition, you can easily do the 2-view matching demo through
`notebooks/demo.ipynb` .
## Training
Training code can be found in `train_lfnet.py`. We will **not** provide any
support for the training process and datasets. All issues related to this topic
will be closed without answers.
## Some Examples
| Outdoor dataset Top: LF-Net, Bottom: SIFT | Indoor dataset Top: LF-Net, Bottom: SIFT | Webcam datasetTop: LF-Net, Bottom: SIFT |
|:---------|:--------------------|:----------------|
|  |  |  |