https://github.com/iago-suarez/efficient-descriptors
:rocket::rocket: Revisiting Binary Local Image Description for Resource Limited Devices
https://github.com/iago-suarez/efficient-descriptors
computer-vision descriptors local-features real-time robotics slam
Last synced: about 1 month ago
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:rocket::rocket: Revisiting Binary Local Image Description for Resource Limited Devices
- Host: GitHub
- URL: https://github.com/iago-suarez/efficient-descriptors
- Owner: iago-suarez
- Created: 2021-08-10T17:56:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-10-21T19:15:15.000Z (over 1 year ago)
- Last Synced: 2025-05-02T16:03:33.694Z (about 2 months ago)
- Topics: computer-vision, descriptors, local-features, real-time, robotics, slam
- Language: C
- Homepage: https://iago-suarez.com/efficient-descriptors
- Size: 43.8 MB
- Stars: 170
- Watchers: 8
- Forks: 27
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Revisiting Binary Local Image Description for Resource Limited Devices
**📢IMPORTANT📢 BAD is now available in OpenCV as [cv::xfeatures2d::TEBLID](https://docs.opencv.org/4.x/dd/dc1/classcv_1_1xfeatures2d_1_1TEBLID.html)**
This repository contains the source code of **BAD** (renamed as [TEBLID](https://docs.opencv.org/4.x/dd/dc1/classcv_1_1xfeatures2d_1_1TEBLID.html) after its inclusion in OpenCV contrib) and **HashSIFT** descriptors presented in "[Revisiting Binary Local Image Description for Resource Limited Devices](https://arxiv.org/pdf/2108.08380.pdf)". When accuracy and efficiency are both important, the descriptors in this repository offer the perfect trade-off for real-time applications and resource limited devices like smartphones, robots or drones.
## Dependencies
The code depends on OpenCV 4.
To install OpenCV ... In Ubuntu 18.04 compile it from sources with the following instructions:
```shell script
# Install dependencies (Ubuntu 18.04)
sudo apt-get install -y build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
# Download source code
git clone https://github.com/opencv/opencv.git --branch 4.5.2 --depth 1
# Create build directory
cd opencv && mkdir build && cd build
# Generate makefiles, compile and install
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
make -j
sudo make install
```
* NOTE: The code also compile with OpenCV 3, but without parallel for's. Thus, the description will be slower.
## Compile and Run
We provide the pre-trained execution code of BAD and HashSIFT descriptors.
The code contains two demos. The first one estimates the fundamental matrix between
two images of the Machine Hall 05 from [EuRoC MAV Dataset](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets).
The demo detects feature points using ORB detector (FAST + Harris score) and describes using BAD.
We draw the RANSAC inliers with less than 3px of epipolar error.
The code can be compiled with Cmake:
```shell script
mkdir build && cd build
cmake .. && make
./stereo_demo [hashsift]
```
We also show a second demo that registers a pair of planar images.
```shell script
./homography_demo [hashsift]
```
The result for the provided images should be several imshows and something like this in the standard output:
```
*************** Homography estimation demo ***************
Detected features: 1000
Matched features: 64
Inliers percentage: 6.4%
```
If ORB descriptor is used instead of BAD, only 2.4% of inliers are obtained.
## References
If you use this code, you must cite our Robotics and Automation Letters paper:
```bibtex
@ARTICLE{9521740,
author={Su\'arez, Iago and Buenaposada, Jos\'e M. and Baumela, Luis},
journal={IEEE Robotics and Automation Letters},
title={Revisiting Binary Local Image Description for Resource Limited Devices},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/LRA.2021.3107024}}
```
## Contact and Licence
We provide a free pre-trained version of the execution code. Full execution and training code can be obtained under license, if you are interested please contact us:
* Iago Suárez ( [email protected] ) for technical issues.
* Miguel Ángel Orellana Sainz ( [email protected] ) for commercial issues.
This software was developed by [The Graffter S.L.](http://www.thegraffter.com) in collaboration with the [PCR lab of the Universidad Politécnica de Madrid](http://www.dia.fi.upm.es/~pcr/research.html).