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code for paper Differentiable Surface Splatting for Point-based Geometry Processing
+ Mar 2021: major updates tag 2.0.
+ > Now supports simultaneous normal and point position updates.
+ > Unified learning rate using Adam optimizer.
+ > Highly optimized cuda operations
+ > Shares pytorch3d structure- install prequisitories. Our code uses python3.8, pytorch 1.6.1, pytorch3d. the installation instruction requires the latest anaconda.
# install cuda, cudnn, nccl from nvidia
# we tested with cuda 10.2 and pytorch 1.6.0
# update conda
conda update -n base -c defaults conda
# install requirements
conda create -n pytorch3d python=3.8
conda config --add channels pytorch
conda config --add channels conda-forge
conda activate pytorch3d
conda install -c pytorch pytorch=1.6.0 torchvision cudatoolkit=10.2
conda install -c conda-forge -c fvcore -c iopath fvcore iopath
conda install -c bottler nvidiacub
conda install pytorch3d -c pytorch3d
conda install --file requirements.txt
pip install "git+https://github.com/mmolero/pypoisson.git"- clone and compile
git clone --recursive https://github.com/yifita/DSS.git
cd dss
# compile external dependencies
cd external/prefix
python setup.py install
cd ../FRNN
python setup.py install
cd ../torch-batch-svd
python setup.py install
# compile library
cd ../..
python setup.py develop# create mvr images using intrinsics defined in the script
python scripts/create_mvr_data_from_mesh.py --points example_data/mesh/yoga6.ply --output example_data/images --num_cameras 128 --image-size 512 --tri_color_light --point_lights --has_specular
python train_mvr.py --config configs/dss.ymlCheck the optimization process in tensorboard.
tensorboard --logdir=exp/dss_proj
We will add back this function ASAP.
Please cite us if you find the code useful!
@article{Yifan:DSS:2019,
author = {Yifan, Wang and
Serena, Felice and
Wu, Shihao and
{\"{O}}ztireli, Cengiz and
Sorkine{-}Hornung, Olga},
title = {Differentiable Surface Splatting for Point-based Geometry Processing},
journal = {ACM Transactions on Graphics (proceedings of ACM SIGGRAPH ASIA)},
volume = {38},
number = {6},
year = {2019},
}
We would like to thank Federico Danieli for the insightful discussion, Phillipp Herholz for the timely feedack, Romann Weber for the video voice-over and Derek Liu for the help during the rebuttal. This work was supported in part by gifts from Adobe, Facebook and Snap, Inc.
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