3D Gabor Splatting: Reconstruction of High-frequency Surface Texture using Gabor Noise

Project page | Paper | Video | Gabor Rasterizer (CUDA) | Dataset with COLMAP

This repo contains the official implementation for the paper "3D Gabor Splatting: Reconstruction of High-frequency Surface Texture using Gabor Noise". Our work represents a scene with a set of augumented Gabor kernels (weigthed sum Gabors). We showed our method can reconstruct high-frequency texture more precisely than 2D Gaussian splatting in our paper. We also published our dataset that have images of scenes having high-frequency texture.

Our work is based on 2D Gaussian splatting. Following statements come from 2DGS repository. When you want to know features in more detail, please check the repository and paper.

How to use

# Starting the training process
python train.py -s <path to COLMAP or NeRF Synthetic dataset> 
# View the trained model
python view.py -s <path to COLMAP or NeRF Synthetic dataset> -m <path to trained model> 

Installation

# download
git clone https://github.com/haato-w/3d-gabor-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 gabor_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_ratio=0, for less "disk-aliasing" artifacts.

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

If these arguments are not specified, the script will automatically estimate them using the camera information.

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> --mesh_res 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 --mesh_res 1024
# or use the bounded mesh extraction if you focus on foreground
python render.py -s <path to m360>/<garden> -m output/m360/garden --skip_test --skip_train --mesh_res 1024

If you have downloaded Our dataset, you can use

python train.py -s <path to dataset>/<sweat_pants_full_hd> -m output/sweat_pants_full_hd --resolution 1
python render.py --resolution 1 --skip_test

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

Full evaluation

You can retrieve the result in the paper following scripts after downloading datasets.

python train.py -s {colmap_data_full_path} -m {model_path} --eval --resolution 1 --test_iterations 5000 10000 30000 --save_iterations 5000 10000 30000
python render.py -m {model_path} --skip_mesh --resolution 1
python metrics.py -m {model_path}

Acknowledgements

This project is built upon 3DGS and 2DGS. We thank all the authors for their great repos.

Citation

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

@inproceedings{watanabe3Dgabors,
      title     = {3D Gabor Splatting: Reconstruction of High-frequency Surface Texture using Gabor Noise},
      author    = {Haato Watanabe, Kenji Tojo and Nobuyuki Umetani},
      publisher = {Eurographics Association}, 
      booktitle = {Proceedings of the Eurographics 2025 Short Papers},
      year      = {2025}
}