RaDe-GS: Rasterizing Depth in Gaussian Splatting

RaDe-GS: Rasterizing Depth in Gaussian Splatting

Baowen Zhang, Chuan Fang, Rakesh Shrestha, Yixun Liang, Xiaoxiao Long, Ping Tan

Project page

Thank you for your interest in our work! We have released the training and testing code on dtu. We are currently organizing the remaining code and will release it soon.

1. Installation

Clone this repository.

git clone https://github.com/BaowenZ/RaDe-GS.git --recursive

Install dependencies.

1. create an environment

conda create -n radegs python=3.9
conda activate radegs

2. install pytorch and other dependencies.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

3. install submodules

pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn/

2. Preparation

We use preprocessed DTU dataset from 2DGS for training. And we follow GOF to evaluate the geometry. Point clouds from the DTU dataset need saved to dtu_eval/Offical_DTU_Dataset for the geometry evaluation.

3. Training and Evalution

# training
python train.py -s <path to DTU dataset> -m <output folder> -r 2 --use_decoupled_appearance
# mesh extraction
python mesh_extract.py -s <path to DTU dataset> -m <output folder> -r 2
# evaluation
python evaluate_dtu_mesh.py -s <path to DTU dataset> -m <output folder>

4. Viewer

Current viewer in this repository is very similar to the original Gaussian Splatting viewer (with small modifications for 3D filters). You can build and use it in the same way as Gaussian Splatting.

5. Acknowledge

We build this project based on Gaussian Splatting.

We adopt the loss functions of 2D GS and use the preprocessed DTU dataset.

We adopt the densification strategy, evalution and decoupled appearance modeling form GOF.

We thank the authors of Gaussian Splatting, 2D GS, and GOF for their great works.