Research @ Magic Leap

SuperPoint Weights File and Demo Script

Introduction

This repo contains the pretrained SuperPoint network, as implemented by the originating authors. SuperPoint is a research project at Magic Leap. The SuperPoint network is a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors can thus be used for various image-to-image matching tasks. For more details please see

• Full paper PDF: SuperPoint: Self-Supervised Interest Point Detection and Description

• Presentation PDF: Talk at CVPR Deep Learning for Visual SLAM Workshop 2018

• Authors: Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich

This demo showcases a simple sparse optical flow point tracker that uses SuperPoint to detect points and match them across video sequences. The repo contains two core files (1) a PyTorch weights file and (2) a python deployment script that defines the network, loads images and runs the pytorch weights file on them, creating a sparse optical flow visualization. Here are videos of the demo running on various publically available datsets:

Freiburg RGBD:

KITTI:

Microsoft 7 Scenes:

MonoVO:

Dependencies

• OpenCV python >= 3.4
• PyTorch >= 0.4

This repo depends on a few standard pythonic modules, plus OpenCV and PyTorch. These commands usually work (tested on Mac and Ubuntu) for installing the two libraries:

pip install opencv-python
pip install torch

Running the Demo

This demo will run the SuperPoint network on an image sequence and compute points and descriptors from the images, using a helper class called SuperPointFrontend. The tracks are formed by the PointTracker class which finds sequential pair-wise nearest neighbors using two-way matching of the points' descriptors. The demo script uses a helper class called VideoStreamer which can process inputs from three different input streams:

1. A directory of images, such as .png or .jpg
2. A video file, such as .mp4 or .avi
3. A USB Webcam

Run the demo on provided directory of images in CPU-mode:

./demo_superpoint.py assets/icl_snippet/

You should see the following output from the ICL-NUIM sequence snippet:

Run the demo on provided .mp4 file in GPU-mode:

./demo_superpoint.py assets/nyu_snippet.mp4 --cuda

You should see the following output from the NYU sequence snippet:

Run a live demo via webcam (id #1) in CPU-mode:

./demo_superpoint.py camera --camid=1

Run the demo on a remote GPU (no display) on 640x480 images and write the output to myoutput/

./demo_superpoint.py assets/icl_snippet/ --W=640 --H=480 --no_display --write --write_dir=myoutput/

Additional useful command line parameters

• Use --H to change the input image height (default: 120).
• Use --W to change the input image width (default: 160).
• Use --display_scale to scale the output visualization image height and width (default: 2).
• Use --cuda flag to enable the GPU.
• Use --img_glob to change the image file extension (default: *.png).
• Use --min_length to change the minimum track length (default: 2).
• Use --max_length to change the maximum track length (default: 5).
• Use --conf_thresh to change the point confidence threshold (default: 0.015).
• Use --nn_thresh to change the descriptor matching distance threshold (default: 0.7).
• Use --show_extra to show more computer vision outputs.
• Press the q key to quit.

BibTeX Citation

@inproceedings{detone18superpoint,
  author    = {Daniel DeTone and
               Tomasz Malisiewicz and
               Andrew Rabinovich},
  title     = {SuperPoint: Self-Supervised Interest Point Detection and Description},
  booktitle = {CVPR Deep Learning for Visual SLAM Workshop},
  year      = {2018},
  url       = {http://arxiv.org/abs/1712.07629}
}

Additional Notes

• We do not intend to release the SuperPoint training or evaluation code, please do not email us to ask for it.
• We do not intend to release the Synthetic Shapes dataset used to bootstrap the SuperPoint training, please do not email us to ask for it.
• We use bi-linear interpolation rather than the bi-cubic interpolation described in the paper to sample the descriptor as it is faster and gave us similar results.

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