0iui0 / 2d-gaussian-splatting

[SIGGRAPH'24] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields

2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Project page | Paper | Video | Surfel Rasterizer (CUDA) | Surfel Rasterizer (Python) | DTU+COLMAP (3.5GB) |

This repo contains the official implementation for the paper "2D Gaussian Splatting for Geometrically Accurate Radiance Fields". Our work represents a scene with a set of 2D oriented disks (surface elements) and rasterizes the surfels with perspective correct differentiable raseterization. Our work also develops regularizations that enhance the reconstruction quality. We also devise meshing approaches for Gaussian splatting.

⭐ New Features

• 2024/05/05: Important updates - Now our algorithm supports unbounded mesh extraction! Our key idea is to contract the space into a sphere and then perform adaptive TSDF truncation.

Installation

# download
git clone https://github.com/hbb1/2d-gaussian-splatting.git --recursive

# if you have an environment used for 3dgs, use it
# if not, create a new environment
conda env create --file environment.yml
conda activate surfel_splatting

Training

To train a scene, simply use

python train.py -s <path to COLMAP or NeRF Synthetic dataset>

Commandline arguments for regularizations

--lambda_normal  # hyperparameter for normal consistency
--lambda_distortion # hyperparameter for depth distortion
--depth_ratio # 0 for mean depth and 1 for median depth, 0 works for most cases

Tips for adjusting the parameters on your own dataset:

• For unbounded/large scenes, we suggest using mean depth, i.e., depth_trunc=0, for less "disk-aliasing" artefacts.

Testing

Bounded Mesh Extraction

To export a mesh within a bounded volume, simply use

python render.py -m <path to pre-trained model> -s <path to COLMAP dataset> 

Commandline arguments you should adjust accordingly for meshing for bounded TSDF fusion, use

--depth_ratio # 0 for mean depth and 1 for median depth
--voxel_size # voxel size
--depth_trunc # depth truncation

Unbounded Mesh Extraction

To export a mesh with an arbitrary size, we devised an unbounded TSDF fusion with space contraction and adaptive truncation.

python render.py -m <path to pre-trained model> -s <path to COLMAP dataset> --resolution 1024

Quick Examples

Assuming you have downloaded MipNeRF360, simply use

python train.py -s <path to m360>/<garden> -m output/m360/garden
# use our unbounded mesh extraction!!
python render.py -s <path to m360>/<garden> -m output/m360/garden --unbounded --skip_test --skip_train
# or use the bounded mesh extraction if you focus on foreground
python render.py -s <path to m360>/<garden> -m output/m360/garden -depth_trunc 6 --voxel_size 0.008 --skip_test --skip_train

If you have downloaded the DTU dataset, you can use

python train.py -s <path to dtu>/<scan105> -m output/date/scan105 -r 2 --depth_ratio 1
python render.py -r 2 --depth_ratio 1 --depth_trunc 3 --voxel_size 0.004 --skip_test --skip_train

Custom Dataset: We use the same COLMAP loader as 3DGS, you can prepare your data following here.

Full evaluation

We provide two scripts to evaluate our method of novel view synthesis and geometric reconstruction. For novel view synthesis on MipNeRF360 (which also works for other colmap datasets), use

python scripts/mipnerf_eval.py -m60 <path to the MipNeRF360 dataset>

For geometry reconstruction on DTU dataset, please download the preprocessed data. You also need to download the ground truth DTU point cloud.

python scripts/dtu_eval.py --dtu <path to the preprocessed DTU dataset>   \
     --DTU_Official <path to the official DTU dataset>

Acknowledgements

This project is built upon 3DGS. The TSDF fusion for extracting mesh is based on Open3D. The rendering script for MipNeRF360 is adopted from Multinerf, while the evaluation scripts for DTU and Tanks and Temples dataset are taken from DTUeval-python and TanksAndTemples, respectively. We thank all the authors for their great repos.

Citation

If you find our code or paper helps, please consider citing:

@inproceedings{Huang2DGS2024,
    title={2D Gaussian Splatting for Geometrically Accurate Radiance Fields},
    author={Huang, Binbin and Yu, Zehao and Chen, Anpei and Geiger, Andreas and Gao, Shenghua},
    publisher = {Association for Computing Machinery},
    booktitle = {SIGGRAPH 2024 Conference Papers},
    year      = {2024},
    doi       = {10.1145/3641519.3657428}
}