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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}
}