Stereo Radiance Fields

Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021

Paper - Supplementaty - Video - Project Website - Arxiv - If you find our project useful, please cite us. Citation (Bibtex)

Install

A linux system with python environment manager conda is required for the project.

The following commands clone the repo on your machine and install an environment, "srf", containing all dependencies.

git clone https://github.com/jchibane/srf.git
cd SRF_git
conda env create -f srf_env.yml

Quick Start with Pretrained Model

To synthesise novel views of a pretrained and finetuned model use the following command

python generator.py --config configs/finetune_scan23.txt --generate_specific_samples scan23 --gen_pose 0

where --gen_pose is a camera pose of a video sequence from 0-51 (including both). We also provide a second model that can be used by switching both "23" to "106" in the previous command.

In order to do a 3D reconstruction please run:

python 3d_reconstruction.py --config configs/finetune_scan106.txt --generate_specific_samples scan106

Note: The above configuration uses a batch of 250 rays to render at once, which assumes a ~48GB GPU. Consider adjusting this number in case of memory issues, by adding --N_rays_test X, with a suited number X to previous command. Smaller batches will lead to increased generation time. Moreover, RAM usage can be reduced by setting --render_factor X (with X=8 or 16) for image generation.

Training

Data Setup

With the next commands the DTU MVS dataset is downloaded and put in place.

wget http://roboimagedata2.compute.dtu.dk/data/MVS/Rectified.zip -P data/
unzip data/Rectified.zip -d data/
mv data/Rectified/* data/DTU_MVS
rmdir data/Rectified

Start Training

Start to train a model with a specific configuration file using:

python trainer.py --config configs/train_DTU.txt

####Fine-tuning Next we optimize a model trained in the previous step on a specific scene given by 11 test images.

Create a new experiment folder containing the trained model:

mkdir logs/start_finetuning_scan23
cp logs/train_DTU/<your-val-min-checkpoint>.tar logs/start_finetuning_scan23/

And start fine-tuning with:

python trainer.py --config configs/start_finetuning_scan23.txt

Initial improvements are obtained at 1k iterations, further improvements are obtained around 3k and 13k iterations. After training, novel view synthesis and 3D reconstruction can be performed as seen in the above quickstart, but specifying the corresponding configuration file.

To fine-tune on a different scan, say with ID X, copy the config file configs/start_finetune_scan23.txt

cp configs/start_finetune_scan23.txt configs/start_finetune_scanX.txt

and change "scan23" to "scanX" in the expname, fine_tune and generate_specific_samples variables of the configuration, where X is the ID of the desired scan. Similarly, you'd need to change "scan23" to "scanX" in the above experiment folder name.

Contact

For questions and comments please contact Julian Chibane via mail.

License

By downloading and using this code you agree to the terms in the LICENSE.

You agree to cite the Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes paper in any documents that report on research using this software or the manuscript.

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Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes. For commercial inquiries, please see above contact information.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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