3D Gaussian Splatting for Real-Time Radiance Field Rendering

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis (* indicates equal contribution)

| Webpage | Full Paper | Video | Other GRAPHDECO Publications | FUNGRAPH project page |

| T&T+DB COLMAP (650MB) | Pre-trained Models (14 GB) | Viewers for Windows (60MB) | Evaluation Images (7 GB) |

This repository contains the official authors implementation associated with the paper "3D Gaussian Splatting for Real-Time Radiance Field Rendering", which can be found here. We further provide the reference images used to create the error metrics reported in the paper, as well as recently created, pre-trained models.

Abstract: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (≥ 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

BibTeX

@Article{kerbl3Dgaussians,
      author       = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
      title        = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
      journal      = {ACM Transactions on Graphics},
      number       = {4},
      volume       = {42},
      month        = {July},
      year         = {2023},
      url          = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
}

Funding and Acknowledgments

This research was funded by the ERC Advanced grant FUNGRAPH No 788065. The authors are grateful to Adobe for generous donations, the OPAL infrastructure from Université Côte d’Azur and for the HPC resources from GENCI–IDRIS (Grant 2022-AD011013409). The authors thank the anonymous reviewers for their valuable feedback, P. Hedman and A. Tewari for proofreading earlier drafts also T. Müller, A. Yu and S. Fridovich-Keil for helping with the comparisons.

NEW FEATURES !

We have limited resources for maintaining and updating the code. However, we have added a few new features since the original release that are inspired by some of the excellent work many other researchers have been doing on 3DGS. We will be adding other features within the ability of our resources.

Update of October 2024: We integrated training speed acceleration and made it compatible with depth regularization, anti-aliasing and exposure compensation. We have enhanced the SIBR real time viewer by correcting bugs and adding features in the Top View that allows visualization of input and user cameras.

Update of Spring 2024: Orange Labs has kindly added OpenXR support for VR viewing.

Step-by-step Tutorial

Jonathan Stephens made a fantastic step-by-step tutorial for setting up Gaussian Splatting on your machine, along with instructions for creating usable datasets from videos. If the instructions below are too dry for you, go ahead and check it out here.

Colab

User camenduru was kind enough to provide a Colab template that uses this repo's source (status: August 2023!) for quick and easy access to the method. Please check it out here.

Cloning the Repository

The repository contains submodules, thus please check it out with

# SSH
git clone git@github.com:graphdeco-inria/gaussian-splatting.git --recursive

or

# HTTPS
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive

Overview

The codebase has 4 main components: - A PyTorch-based optimizer to produce a 3D Gaussian model from SfM inputs - A network viewer that allows to connect to and visualize the optimization process - An OpenGL-based real-time viewer to render trained models in real-time. - A script to help you turn your own images into optimization-ready SfM data sets

The components have different requirements w.r.t. both hardware and software. They have been tested on Windows 10 and Ubuntu Linux 22.04. Instructions for setting up and running each of them are found in the sections below.

Optimizer

The optimizer uses PyTorch and CUDA extensions in a Python environment to produce trained models.

Hardware Requirements

• CUDA-ready GPU with Compute Capability 7.0+
• 24 GB VRAM (to train to paper evaluation quality)
• Please see FAQ for smaller VRAM configurations

Software Requirements

• Conda (recommended for easy setup)
• C++ Compiler for PyTorch extensions (we used Visual Studio 2019 for Windows)
• CUDA SDK 11 for PyTorch extensions, install after Visual Studio (we used 11.8, known issues with 11.6)
• C++ Compiler and CUDA SDK must be compatible

Setup

Local Setup

Our default, provided install method is based on Conda package and environment management:

SET DISTUTILS_USE_SDK=1 # Windows only
conda env create --file environment.yml
conda activate gaussian_splatting

Please note that this process assumes that you have CUDA SDK 11 installed, not 12. For modifications, see below.

Tip: Downloading packages and creating a new environment with Conda can require a significant amount of disk space. By default, Conda will use the main system hard drive. You can avoid this by specifying a different package download location and an environment on a different drive:

conda config --add pkgs_dirs <Drive>/<pkg_path>
conda env create --file environment.yml --prefix <Drive>/<env_path>/gaussian_splatting
conda activate <Drive>/<env_path>/gaussian_splatting

Modifications

If you can afford the disk space, we recommend using our environment files for setting up a training environment identical to ours. If you want to make modifications, please note that major version changes might affect the results of our method. However, our (limited) experiments suggest that the codebase works just fine inside a more up-to-date environment (Python 3.8, PyTorch 2.0.0, CUDA 12). Make sure to create an environment where PyTorch and its CUDA runtime version match and the installed CUDA SDK has no major version difference with PyTorch's CUDA version.

Known Issues

Some users experience problems building the submodules on Windows (cl.exe: File not found or similar). Please consider the workaround for this problem from the FAQ.

Running

To run the optimizer, simply use

python train.py -s <path to COLMAP or NeRF Synthetic dataset>

Command Line Arguments for train.py

#### --source_path / -s Path to the source directory containing a COLMAP or Synthetic NeRF data set. #### --model_path / -m Path where the trained model should be stored (output/<random> by default). #### --images / -i Alternative subdirectory for COLMAP images (images by default). #### --eval Add this flag to use a MipNeRF360-style training/test split for evaluation. #### --resolution / -r Specifies resolution of the loaded images before training. If provided 1, 2, 4 or 8, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target. #### --data_device Specifies where to put the source image data, cuda by default, recommended to use cpu if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training. Thanks to HrsPythonix. #### --white_background / -w Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset. #### --sh_degree Order of spherical harmonics to be used (no larger than 3). 3 by default. #### --convert_SHs_python Flag to make pipeline compute forward and backward of SHs with PyTorch instead of ours. #### --convert_cov3D_python Flag to make pipeline compute forward and backward of the 3D covariance with PyTorch instead of ours. #### --debug Enables debug mode if you experience erros. If the rasterizer fails, a dump file is created that you may forward to us in an issue so we can take a look. #### --debug_from Debugging is slow. You may specify an iteration (starting from 0) after which the above debugging becomes active. #### --iterations Number of total iterations to train for, 30_000 by default. #### --ip IP to start GUI server on, 127.0.0.1 by default. #### --port Port to use for GUI server, 6009 by default. #### --test_iterations Space-separated iterations at which the training script computes L1 and PSNR over test set, 7000 30000 by default. #### --save_iterations Space-separated iterations at which the training script saves the Gaussian model, 7000 30000 <iterations> by default. #### --checkpoint_iterations Space-separated iterations at which to store a checkpoint for continuing later, saved in the model directory. #### --start_checkpoint Path to a saved checkpoint to continue training from. #### --quiet Flag to omit any text written to standard out pipe. #### --feature_lr Spherical harmonics features learning rate, 0.0025 by default. #### --opacity_lr Opacity learning rate, 0.05 by default. #### --scaling_lr Scaling learning rate, 0.005 by default. #### --rotation_lr Rotation learning rate, 0.001 by default. #### --position_lr_max_steps Number of steps (from 0) where position learning rate goes from initial to final. 30_000 by default. #### --position_lr_init Initial 3D position learning rate, 0.00016 by default. #### --position_lr_final Final 3D position learning rate, 0.0000016 by default. #### --position_lr_delay_mult Position learning rate multiplier (cf. Plenoxels), 0.01 by default. #### --densify_from_iter Iteration where densification starts, 500 by default. #### --densify_until_iter Iteration where densification stops, 15_000 by default. #### --densify_grad_threshold Limit that decides if points should be densified based on 2D position gradient, 0.0002 by default. #### --densification_interval How frequently to densify, 100 (every 100 iterations) by default. #### --opacity_reset_interval How frequently to reset opacity, 3_000 by default. #### --lambda_dssim Influence of SSIM on total loss from 0 to 1, 0.2 by default. #### --percent_dense Percentage of scene extent (0--1) a point must exceed to be forcibly densified, 0.01 by default.

Note that similar to MipNeRF360, we target images at resolutions in the 1-1.6K pixel range. For convenience, arbitrary-size inputs can be passed and will be automatically resized if their width exceeds 1600 pixels. We recommend to keep this behavior, but you may force training to use your higher-resolution images by setting -r 1.

The MipNeRF360 scenes are hosted by the paper authors here. You can find our SfM data sets for Tanks&Temples and Deep Blending here. If you do not provide an output model directory (-m), trained models are written to folde