PEP-GS

Perceptually-Enhanced Precise Structured 3D Gaussians for View-Adaptive Rendering

🌐 Project Page

Installation

conda create -n po_gs python=3.8
conda activate po_gs
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

Dataset

Mip-NeRF 360 dataset download link: Mip-NeRF 360

Tanks&Temples and Deep Blending datasets download link: Tanks&Temples and Deep Blending

Data

The current directory should contain the following folders:

PO-GS
├───data/
    ├── dataset_name
    │   ├── scene1/
    │   │   ├── images
    │   │   │   ├── IMG_0.jpg
    │   │   │   ├── IMG_1.jpg
    │   │   │   ├── ...
    │   │   ├── sparse/
    │   │       └──0/
    │   ├── scene2/
    │   │   ├── images
    │   │   │   ├── IMG_0.jpg
    │   │   │   ├── IMG_1.jpg
    │   │   │   ├── ...
    │   │   ├── sparse/
    │   │       └──0/
    ...

Training

Similar to Scaffold-GS, we provide batch training scripts:

# Mip-NeRF 360
bash train_mip360.sh
# Tanks and Temples
bash train_tnt.sh
# Deep Blending
bash train_db.sh

Evaluation

For evalution, you can use the following command:

python render.py -m <path to trained model> 
python metrics.py -m <path to trained model>