Skip to content
This repository has been archived by the owner on Sep 2, 2024. It is now read-only.

isl-org/StableViewSynthesis

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DISCONTINUATION OF PROJECT

This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

Stable View Synthesis

Code repository for "Stable View Synthesis".

Setup

Install the following Python packages in your Python environment

- numpy (1.19.1)
- scikit-image (0.15.0)
- pillow (7.2.0)
- torch
- torchvision (0.7.0)
- torch-scatter (1.6)
- torch-sparse (1.6)
- torch-geometric (1.6)
- torch-sparse (1.6)
- open3d (0.11)
- python-opencv
- matplotlib (3.2.x)
- pandas (1.0.x)

To compile the Python extensions you will also need Eigen and cmake.

Clone the repository and initialize the submodule

git clone https://github.com/intel-isl/StableViewSynthesis.git
cd StableViewSynthesis
git submodule update --init --recursive

Finally, build the Python extensions

cd ext/preprocess
cmake -DCMAKE_BUILD_TYPE=Release .
make 

cd ../mytorch
python setup.py build_ext --inplace

Tested with Ubuntu 18.04 and macOS Catalina.

Run Stable View Synthesis

Make sure you adapted the paths in config.py to point to the downloaded data!

cd experiments

Then run the evaluation via

python exp.py --net resunet3.16_penone.dirs.avg.seq+9+1+unet+5+2+16.single+mlpdir+mean+3+64+16 --cmd eval --iter last --eval-dsets tat-subseq

This will run the pretrained network on the four Tanks and Temples sequences.

To train the network from scratch you can run

python exp.py --net resunet3.16_penone.dirs.avg.seq+9+1+unet+5+2+16.single+mlpdir+mean+3+64+16 --cmd retrain

Data

See FreeViewSynthesis.

Citation

Please cite our paper if you find this work useful.

@inproceedings{Riegler2021SVS,
  title={Stable View Synthesis},
  author={Riegler, Gernot and Koltun, Vladlen},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}

Video

Stable View Synthesis Video

About

No description, website, or topics provided.

Resources

License

Security policy

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published