https://github.com/BoChenYS/BPnP

Back-propagatable PnP
https://github.com/BoChenYS/BPnP

Last synced: 8 days ago
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Back-propagatable PnP

• Host: GitHub
• URL: https://github.com/BoChenYS/BPnP
• Owner: BoChenYS
• License: mit
• Created: 2019-09-22T04:28:37.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-28T19:32:01.000Z (almost 4 years ago)
• Last Synced: 2024-08-01T13:28:59.940Z (3 months ago)
• Language: Python
• Homepage:
• Size: 670 KB
• Stars: 303
• Watchers: 14
• Forks: 34
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
This repo provides the code used in the paper

# [End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization (CVPR 2020)](https://arxiv.org/pdf/1909.06043.pdf)

![](demo_data/cover.png)

## Watch our video demo

[![Watch the video](demo_data/video.png)](https://youtu.be/eYmoAAsiBEE)

## Install

`bash requirements.sh`

## Back-propagatable PnP (BPnP)

Using BPnP is easy. Just add the following line in your code

````bash

import BPnP

bpnp = BPnP.BPnP.apply

````

Then you can use it as any autograd function in Pytorch.

## Demo experiments

To see the demos presented in the paper, run

````bash

python demoPoseEst.py

````

or

````bash

python demoSfM.py

````

or

````bash

python demoCamCali.py

````

## Cite this work

````

@inproceedings{BPnP2020,

    Author = {Chen, Bo and Parra, Alvaro and Cao, Jiewei and Li, Nan and Chin, Tat-Jun},

    Title = {End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization},

    Booktitle = {CVPR},

    Year = {2020}}

````