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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
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Full paper PDF: SuperPoint: Self-Supervised Interest Point Detection and Description
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Presentation PDF: Talk at CVPR Deep Learning for Visual SLAM Workshop 2018
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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:
<img src="assets/processed_freiburg.gif" width="240">
KITTI:
<img src="assets/processed_kitti.gif" width="480">
Microsoft 7 Scenes:
<img src="assets/processed_ms7.gif" width="240">
MonoVO:
<img src="assets/processed_monovo.gif" width="240">
Dependencies
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:
- A directory of images, such as .png or .jpg
- A video file, such as .mp4 or .avi
- 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:
<img src="assets/processed_icl.gif" width="160">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:
<img src="assets/processed_nyu.gif" width="160">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.
Legal Disclaimer
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