# BPnP

**Repository Path**: skylook/BPnP

## Basic Information

- **Project Name**: BPnP
- **Description**: Back-propagatable PnP
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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}}
````