Gaussian in the Dark 😈

This repository contains the official authors implementation associated with the paper "Gaussian in the Dark: Real-Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting", which has been accepted by Pacific Graphics 2024 (journal track).

Requirements

Linux OS
NVIDIA GPUs. We experimented on A6000 GPUs (cuda 11.8).
Python libraries: see environment.yml. You can use the following commands with Anaconda3 to create and activate your virtual environment:
- git clone https://github.com/yec22/Gaussian-DK.git
- cd Gaussian-DK
- conda env create -f environment.yml
- conda activate 3dgs_dk

Dataset

To facilitate further research on novel view synthesis in dark conditions, we propose a new challenging dataset containing 12 real-world scenes (5 indoors and 7 outdoors). Each scene consists of approximately 80 to 130 regular format images.

The proposed dataset can be downloaded from here (~13G).

Usage

First, please make sure that all requirements are satisfied. Then, following the scripts below to train, render, and evaluate.

# Train with train/test split
python train.py -r 4 -s datasets/dark/piano -m output/piano --port 1111 --eval

# Generate renderings
python render.py -m output/piano

# Compute metrics on renderings
python metrics.py -m output/piano

# More visualization
python render_spherify.py -m output/piano

Results

Comparison with 3DGS

Light-Up Effect

Acknowledgement