DeepSurfels
This is the official implementation of the CVPR 2021 submission DeepSurfels: Learning Online Appearance Fusion
DeepSurfels is a novel 3D representation for geometry and appearance information that combines planar surface primitives with voxel grid representation for improved scalability and rendering quality.
If you find our code or paper useful, please consider citing
@InProceedings{DeepSurfels:CVPR:21,
title = {{DeepSurfels}: Learning Online Appearance Fusion},
author = {Mihajlovic, Marko and Weder, Silvan and Pollefeys, Marc and Oswald, Martin R.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
}
Contact Marko Mihajlovic for questions or open an issue / a pull request.
Installation
The prerequest is to install python packages specified in the requirements.txt file, which can conveniently
accomplished by using an Anaconda environment.
# clone the repo
git clone https://github.com/onlinereconstruction/deep_surfels.git
cd ./deep_surfels
# create environment
conda env create -f environment.yml
conda activate deep_surfels
Then install the deep_surfel package via pip
pip install ./deep_surfel
Data
Directory ./data_prep/data_samples contains preprocessed toy data samples.
See ./data_prep/from_depth_frames.py on how to prepare your own dataset.
Usage
To run the deterministic fusion:
cd appearance_fusion
python test.py -c ../configurations/sample_deterministic.yml --extract_meshes
To trained the learned module:
python train.py -c ../configurations/sample.yml
To evaluate the trained module:
python test.py -c ../configurations/sample.yml --extract_meshes
The rendered images will be stored in the specified logging_root_dir directory.
See ./appearance_fusion/config.py for all available configuration parameters.
