GauStudio is a modular framework that supports and accelerates research and development in the rapidly advancing field of 3D Gaussian Splatting (3DGS) and its diverse applications.
Here's an improved version of the text with better structure and flow: # Dataset ## [Download from Baidu Netdisk](https://pan.baidu.com/s/157mqM6C5Wy30DY3aip2NeA?pwd=ig3v#list/path=%2F) | [Download from Hugging Face(Comming Soon)]() To comprehensively evaluate the robustness of 3DGS methods under diverse lighting conditions, materials, and geometric structures, we have curated the following datasets: ## 1. Collection of 5 Synthetic Datasets in COLMAP Format We have integrated 5 synthetic datasets: *nerf_synthetic, refnerf_synthetic, nero_synthetic, nsvf_synthetic, and BlendedMVS*, totaling 143 complex real-world scenes. To ensure compatibility, we have utilized COLMAP to perform feature matching and triangulation based on the original poses, uniformly converting all data to the COLMAP format. ## 2. Real-world Scenes with High-quality Normal Annotations and LoFTR Initialization * **COLMAP-format [MuSHRoom](https://github.com/TUTvision/MuSHRoom/tree/main)**: To address the difficulty of acquiring indoor scene data such as ScanNet++, we have processed and generated COLMAP-compatible data based on the publicly available MuSHRoom dataset. Please remember to use this data under the original license. * **More Complete Tanks and Temples**: To address the lack of ground truth poses in the Tanks and Temples test set, we have converted the pose information provided by MVSNet to generate COLMAP-format data. This supports algorithm evaluation on a wider range of indoor and outdoor scenes. The leaderboard submission script will be released in a subsequent version. * **Normal Annotations and LoFTR Initialization** To tackle modeling challenges such as sparse viewpoints and specular highlight regions, we have annotated high-quality, temporally consistent normal data based on our private model, providing new avenues for indoor and unbounded scene 3DGS reconstruction. The annotation code will be released soon. Additionally, we provide LoFTR-based initial point clouds to support better initialization. # Installation Before installing the software, please note that the following steps have been tested on Ubuntu 20.04. If you encounter any issues during the installation on Windows, we are open to addressing and resolving such issues. ## Prerequisites * NVIDIA graphics card with at least 6GB VRAM * CUDA installed * Python >= 3.8 ## Optional Step: Create a Conda Environment It is recommended to create a conda environment before proceeding with the installation. You can create a conda environment using the following commands: ```sh # Create a new conda environment conda create -n gaustudio python=3.8 # Activate the conda environment conda activate gaustudio ``` ## Step 1: Install PyTorch You will need to install PyTorch. The software has been tested with torch1.12.1+cu113 and torch2.0.1+cu118, but other versions should also work fine. You can install PyTorch using conda as follows: ``` # Example command to install PyTorch version 1.12.1+cu113 conda install pytorch=1.12.1 torchvision=0.13.1 cudatoolkit=11.3 -c pytorch # Example command to install PyTorch version 2.0.1+cu118 pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 ``` ## Step 2: Install Dependencies Install the necessary dependencies by running the following command: ```sh pip install -r requirements.txt ``` ## Step 3: Install Customed Rasterizer and Gaustudio ``` cd submodules/gaustudio-diff-gaussian-rasterization python setup.py install cd ../../ python setup.py develop ``` ## Optional Step: Install PyTorch3D If you require mesh rendering and further mesh refinement, you can install PyTorch3D follow the [link](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md): # QuickStart ## Mesh Extraction for 3DGS