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
A linux system with python environment manager conda and a full and system wide installation of the CUDA Toolkit 10.1 is required for the project (the latter only for compilation of the torchsearchsorted library).
The following commands clone the repo on your machine and install an environment, "srf", containing all dependencies.
git clone ADD LINK cd SRF_git conda env create -f srf_env.yml
Please close the terminal session at this point, and reopen it at the same location. This is done to ensure conda correctly loads all packages.
conda activate srf pip install torchsearchsorted/
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
Quick Start with Pretrained Model
To synthesise novel views use the following command
python generator.py --config configs/finetune_scan23.txt --video --render_factor 8 --generate_specific_samples scan23 --fixed_batch 1 --ft_path checkpoint.tar --gen_pose 0
--config specifies the path to the experiment configuration and
--gen_pose is the frame number from 0-55 (including both).