# gaussian-splatting: Real-Time 3D Radiance Field Rendering

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gaussian-splatting is the original reference implementation for real-time radiance field rendering. This repository introduces a novel approach using 3D Gaussians for high-quality, real-time novel-view synthesis at 1080p resolution, offering significant advancements in computer graphics and vision. Developed by GRAPHDECO Inria, it provides a robust framework for 3D scene reconstruction and interactive visualization.

GitHub: https://github.com/graphdeco-inria/gaussian-splatting
OSRepos URL: https://osrepos.com/repo/graphdeco-inria-gaussian-splatting

## Summary

gaussian-splatting is the original reference implementation for real-time radiance field rendering. This repository introduces a novel approach using 3D Gaussians for high-quality, real-time novel-view synthesis at 1080p resolution, offering significant advancements in computer graphics and vision. Developed by GRAPHDECO Inria, it provides a robust framework for 3D scene reconstruction and interactive visualization.

## Topics

- computer-graphics
- computer-vision
- radiance-field
- Python
- 3D-reconstruction
- real-time-rendering
- neural-rendering

## Repository Information

Last analyzed by OSRepos: Fri Feb 13 2026 08:01:26 GMT+0000 (Western European Standard Time)
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## Content

## Introduction
The `gaussian-splatting` repository presents the original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering," a groundbreaking method developed by GRAPHDECO Inria. This innovative approach revolutionizes novel-view synthesis by achieving state-of-the-art visual quality and real-time performance at 1080p resolution. It leverages 3D Gaussians to represent scenes, optimizing anisotropic covariance and employing a fast, visibility-aware rendering algorithm to deliver unparalleled speed and fidelity in 3D reconstruction and rendering.

## Installation
To get started with `gaussian-splatting`, begin by cloning the repository, ensuring to include submodules:
bash
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive

The project primarily uses Python with PyTorch and CUDA extensions. A Conda environment is highly recommended for managing dependencies. Hardware requirements include a CUDA-ready GPU with Compute Capability 7.0+ and at least 24 GB VRAM for full training quality, though smaller VRAM configurations are possible with adjustments. Detailed setup instructions for both Windows and Ubuntu Linux, including C++ compiler and CUDA SDK requirements, are provided in the official documentation.

## Examples
The `gaussian-splatting` codebase offers a comprehensive suite of tools for training, rendering, and evaluation:
*   **Optimizer:** Use `python train.py -s <path to dataset>` to train 3D Gaussian models from SfM inputs.
*   **Evaluation:** Generate renderings and compute error metrics with `python render.py` and `python metrics.py` after training, optionally using a train/test split with the `--eval` flag.
*   **Interactive Viewers:** Explore your trained models in real-time using the provided network viewer (for live training visualization) and the OpenGL-based real-time viewer. Pre-built Windows binaries are available for convenience.
*   **Processing Custom Scenes:** Convert your own images into optimization-ready SfM datasets using `python convert.py`, which integrates with COLMAP and ImageMagick.
*   **New Features:** Recent updates include training speed acceleration (using `sparse_adam` optimizer), depth regularization for improved scene reconstruction, exposure compensation for "in the wild" captures, and anti-aliasing with an EWA Filter. The SIBR viewers also feature a "Top View" for visualizing camera placement and an experimental OpenXR branch for VR support.

## Why Use
`gaussian-splatting` stands out for its ability to produce exceptionally high visual quality for novel-view synthesis while maintaining real-time rendering speeds, a significant challenge for previous radiance field methods. Its efficient representation using 3D Gaussians and optimized rendering pipeline make it ideal for applications requiring interactive 3D experiences, such as virtual reality, augmented reality, and advanced computer graphics. The project's active development, robust feature set, and strong academic backing from Inria position it as a leading solution in the field of neural rendering and 3D reconstruction.

## Links
*   **GitHub Repository:** [https://github.com/graphdeco-inria/gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting){:target="_blank"}
*   **Project Webpage:** [https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/){:target="_blank"}
*   **Full Paper:** [https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf){:target="_blank"}
*   **Video Overview:** [https://youtu.be/T_kXY43VZnk](https://youtu.be/T_kXY43VZnk){:target="_blank"}
*   **Pre-trained Models:** [https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip){:target="_blank"}
*   **Windows Viewers:** [https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip){:target="_blank"}
*   **Colab Template:** [https://github.com/camenduru/gaussian-splatting-colab](https://github.com/camenduru/gaussian-splatting-colab){:target="_blank"}
*   **Step-by-step Tutorial:** [https://www.youtube.com/watch?v=UXtuigy_wYc](https://www.youtube.com/watch?v=UXtuigy_wYc){:target="_blank"}