[CVPR 2025] LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors [Paper]

Han Zhou, Wei Dong^†, Jun Chen

McMaster University, ^†Corresponding Author

Introduction

This repository represents the official implementation of our CVPR 2025 paper titled LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you for your interest.

📢 News

2025-06-12 We strat to update this repo from today, and we plan to make it complete within one week!

Overall Framework

🛠️ Setup

The code was tested on:

• RTX 5090, Python 3.9, CUDA 12.8, PyTorch 2.8 + cu12.8.

📦 Repository

Clone the repository (requires git):

git clone https://github.com/LowLevelAI/LITA-GS.git
cd LITA-GS

💻 Dependencies

• Create the Conda environment:

  conda create -n litags python=3.9
  conda activate litags

• Then install dependencies:

  • Install Pytorch

  pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128

  • Set Cudatoolkit to 12.8

  export PATH=/usr/local/cuda-12.8/bin:$PATH
  export LD_LIBRARY_PATH=/usr/local/cuda-12.8/lib64:$LD_LIBRARY_PATH

  • Install dependencies

  pip install trimesh tqdm mmcv==1.6.0 scipy scikit-image
  pip install submodules/diff-gaussian-rasterization
  pip install submodules/simple-knn