Awesome
BP-Layers
This repository contains the implementation for our publication "Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems". If you use this implementation please cite the following publication:
@InProceedings{Knobelreiter_2020_CVPR,
author = {Knöbelreiter, Patrick and Sormann, Christian and Shekhovtsov, Alexander and Fraundorfer, Friedrich and Pock, Thomas},
title = {Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
Repository Structure
The repository is structured as follows: - the base directory contains scripts for running inference and python implementations of the networks - 'data' includes sample images for stereo/semantic/flow inference - 'ops' contains custom PyTorch-Ops which need to be installed before running the respective stereo/semantic implementation (note that this is also taken care of by the run_*.sh scripts)
For your convenience, the required libraries are added as submodules. To clone them issue the command
git submodule update --init --recursive
Dependencies
- Cuda 10.2
- pytorch >= 1.3
- argparse
- imageio (with libpfm installed)*
- numpy
The stereo results are saved as pfm images. If your imageio does not have libpfm installed automatically, execute the following command in a python:
imageio.plugins.freeimage.download()
In order to display pfm files we highly recommend the tool provided by the Middlebury stereo benchmark. You can find it <a href="https://github.com/roboception/cvkit">here</a>.
Running the implementation
After installing all of the required dependencies above you need to install the provided modules to you python environment. This can be done with
cd ops
python setup.py install
This will install the SAD matching kernels for stereo and Optical flow. The BP-Layer is installed automatically upon execution of the provided shell scripts. The following sections show how to use them.
Stereo
- run_stereo_sf.sh: The models trained on the Scene-Flow Dataset
- run_stereo_mb.sh: The model used for evaluation on the Middlebury dataset
- run_stereo_kitti: The model used for evaluation on the Kitti dataset
Flow
- run_flow.sh
Semantic Segmentation
- run_semantic_global.sh: Our semantic segmentation model with global pairwise weights
- run_semantic_pixel: Our semantic segmentation model with pixel-wise pairwise weights
sh run_semantic_pixel.sh
Should yield this result:
<p align="center"><img width=45% src="github_imgs/sem_input.png"> <img width=45% src="github_imgs/sem_pred.png"></p>Inside these scripts you can also specify different images to be used for inference. The correct PyTorch-Ops are also automatically installed by these scripts before running the inference.