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)
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
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.
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 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.
For questions and comments please contact Julian Chibane via mail.
Copyright (c) 2021 Julian Chibane, Max-Planck-Gesellschaft
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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