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Extreme View Synthesis

Paper | Extended Presentation at GTC 2019 (requires free registration) | Latex citation

Code for the paper:
Extreme View Synthesis
Inchang Choi, Orazio Gallo, Alejandro Troccoli, Min H. Kim and Jan Kautz, IEEE International Conference on Computer Vision, 2019 (Oral).

License

Licensed under the NVIDIA Source Code License

Pre-requisites

For convenience, we provide a Dockerfile to build a container image to run the code. The image will contain the Python dependencies and a build of COLMAP.

Your system will need:

Docker (>= 19.03)
NVIDIA Docker
NVIDIA GPU driver 418 or later.

Build the container image:

docker build -t xtreme-view .

Download the models

You can download the models from the NVIDIA GPU CLOUD registry using:

./download_model.sh

Running the code

Place your sequence of images in a directory tree with root data, followed by a directory per sequence, e.g., data/0000, and place all images in the sequence into the data/0000/images sub-directory.

Launch the container using the provided script:

./launch_container.sh

Run COLMAP on a sequence of images to get the camera parameters:

./run_colmap.sh /data/0000

Run the extreme view code generation:

python run_xtreme_view.py /data/0000 --input_views=6,8

This will run the extreme view synthesis code using images 6 and 8 of the sequence /data/0000. You can modify the code to use different virtual cameras.

You can run COLMAP and the extreme view synthesis on all the sample sequences:

./run_colmap_all.sh
./run_xtreme_view_all.sh

The results are stored in the sequence directory under xtreme-view. For example, for data/0000 you will find the results in the directory data/0000/xtreme-view. The initial view synthesis is located under output and the the refined one under refinement.

<a name="citation"></a> Citation

If you find this code useful in your research or fun project, please consider citing the paper:

@inproceedings{extremeview,  
  title={Extreme View Synthesis},  
  author={Choi, Inchang and Gallo, Orazio and Troccoli, Alejandro and Kim, Min H and Kautz, Jan},  
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},  
  pages={7781--7790},  
  year={2019}  
}