FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks
This repository contains the code (in PyTorch) for the “FADNet++” paper.
Contents
Introduction
We propose an efficient and accurate deep network for disparity estimation named FADNet with three main features:
- It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation.
- It combines the residual structures to make the deeper model easier to learn.
- It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy.
Usage
Dependencies
Package Installation
- Execute “sh compile.sh” to compile libraries needed by GANet.
- Enter “layers_package” and execute “sh install.sh” to install customized layers, including Channel Normalization layer and Resample layer.
We also release the docker version of this project, which has been