https://github.com/christian-rauch/super_point_inference

C++ implementation of SuperPoint inference in LibTorch
https://github.com/christian-rauch/super_point_inference

libtorch superpoint

Last synced: 4 days ago
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C++ implementation of SuperPoint inference in LibTorch

• Host: GitHub
• URL: https://github.com/christian-rauch/super_point_inference
• Owner: christian-rauch
• Created: 2020-07-07T16:52:16.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-01-02T23:27:28.000Z (11 months ago)
• Last Synced: 2024-05-06T16:46:06.897Z (7 months ago)
• Topics: libtorch, superpoint
• Language: C++
• Homepage: https://github.com/magicleap/SuperPointPretrainedNetwork
• Size: 5.79 MB
• Stars: 27
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# C++ implementation of SuperPoint inference in LibTorch

This implements [SuperPoint: Self-Supervised Interest Point Detection and Description](https://doi.org/10.1109/CVPRW.2018.00060) in C++ using LibTorch and TorchScript.

| matches | tracks |

| :---: | :---: |

| ![](doc/matches.png) | ![](doc/tracks.png) |

## Installation

This C++ implementation of SuperPoint requires [LibTorch](https://pytorch.org/cppdocs/installing.html). The following installation instructions will use [vcstool](http://wiki.ros.org/vcstool), [rosdep](http://wiki.ros.org/rosdep) and [colcon](https://colcon.readthedocs.io) for managing source and binary dependencies and to create a relocatable workspace.

1. CUDA and cuDNN:

    - add the [official CUDA Toolkit repo for Ubuntu](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu)

    - install CUDA and cuDNN runtime `sudo apt install cuda-runtime-11-3 libcudnn8`

2. vcstool, rosdep and colcon:

    ```sh

    sudo pip3 install -U vcstool rosdep colcon-common-extensions

    sudo rosdep init

    rosdep update

    ```

3. create and compile the colcon workspace:

    ```sh

    # create workspace folder

    mkdir ~/super_point_ws/

    cd ~/super_point_ws/

    # download sources

    vcs import << EOF

    - git: {local-name: src/torch_cpp,             uri: "https://github.com/christian-rauch/torch_cpp.git"}

    - git: {local-name: src/super_point_inference, uri: "https://github.com/christian-rauch/super_point_inference.git"}

    EOF

    # resolve binary dependencies

    rosdep install --from-paths src --ignore-src -y

    # build workspace

    colcon build --cmake-args -D CMAKE_BUILD_TYPE=Release -D CMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc

    ```

The `torch_cpp` and `super_point_inference` packages will be installed to `~/super_point_ws/install/`. After sourcing the workspace via `source ~/super_point_ws/install/setup.bash` those packages will be discoverable by CMake.

## Keypoint Extraction and Matching Example

The `super_point_inference` contains the [original model and weights](https://github.com/magicleap/SuperPointPretrainedNetwork) converted via TorchScript and an example program to extract keypoints and find matches in a sequence of images. The converted model and weights are located at `~/super_point_ws/install/super_point_inference/share/weights/SuperPointNet.pt`. The example program `superpoint_match` takes the path to the converted model file and a list of image paths as argument:

```sh

# download example data

wget https://raw.githubusercontent.com/magicleap/SuperPointPretrainedNetwork/master/assets/icl_snippet/{250,254,258}.png -P /tmp/images/

# source the workspace

source ~/super_point_ws/install/setup.bash

# extract keypoints and matches

superpoint_match ~/super_point_ws/install/super_point_inference/share/weights/SuperPointNet.pt /tmp/images/{250,254,258}.png

```

The matches between consecutive image pairs will be shown in separate windows (press any key to close) and exported as `/tmp/matches-$PAIR.png` and `/tmp/tracks-$PAIR.png`.

## Citation

If you use the original work by Daniel DeTone et al., please cite their work:

```bibtex

@InProceedings{DeTone2018,

  author =    {DeTone, Daniel and Malisiewicz, Tomasz and Rabinovich, Andrew},

  title =     {SuperPoint: Self-Supervised Interest Point Detection and Description},

  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

  year =      {2018}

}

```

If you use this implementation, please cite our work:

```bibtex

@ARTICLE{Rauch2022,

  author={Rauch, Christian and Long, Ran and Ivan, Vladimir and Vijayakumar, Sethu},

  journal={IEEE Robotics and Automation Letters},

  title={Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models},

  year={2022},

  volume={7},

  number={4},

  pages={11394-11401},

  doi={10.1109/LRA.2022.3200177}

}

```