DispNetCorr1D-Pytorch

This repository only provides the 1D correlation function needed for DispNetCorr1D in Pytorch. I mainly made this because there are ample resources for Tensorflow implementation of the following papers but the Pytorch implementation have no 1D correlation layer. So I wrote my own.

DispNetCorr1D A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
FlowNet FlowNet: Learning Optical Flow with Convolutional Networks

I only used Pytorch functions so a faster version can be implemented in CUDA. This version was also fast enough for training in my experiments.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

First Install all the relevant requirements given in the requirements.txt file in your pip or anaconda environments.

Dataset

This repo is just a sample so I included one pair of left and right images as an example as shown below

Right Image

Left image

Running the Code

Execute the main.py file.

Tensorflow Version

I replicated the tensor flow version from the following repository.

dispflownet-tf Tensorflow implementation of https://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16

The mentioned repository provides the CUDA version also which is not covered in this repository.

Pytorch Implementation

To make sure my version is exactly equal to the Tensorflow version I had to make sure that both of the following condition are checked:

Forward function
Backward function

Forward Function

As you can confirm by running the program that the Pytorch Version is exactly similar to TF version as shown below:

Backward Function

I made sure using Grad check that gradients were flowing correctly. For this :

1. Set gradCheck = True
2. Set scale = 0.05
3. Run main.py
4. You should be able to see "True" printed after execution which means Grad Check passed.

License

This project is licensed under the MIT License. For specific helper function used in this repository please see the license agreement of the Repo linked in Acknowledgement section

Acknowledgments and Further References

My implementation has been inspired from the following sources.

** dispflownet-tf Tensorflow implementation of https://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16

You can use my function with the following repository:

** FlowNetPytorch Pytorch implementation of FlowNet by Dosovitskiy et al.

** SfmLearner-Pytorch Pytorch version of SfmLearner from Tinghui Zhou et al.

In the above simply modify FlowNet or DispnetS and use my 1D Correlation function to get DispNetCorr1D

iSarmad/DispNetCorr1D-Pytorch