# 3D Gaussian Splatting — Real-Time Radiance Field Rendering

> A rasterization-based approach to novel view synthesis that represents scenes as millions of 3D Gaussians, enabling real-time rendering at high quality from multi-view images.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

# 3D Gaussian Splatting — Real-Time Radiance Field Rendering

## Quick Use
```bash
git clone --recursive https://github.com/graphdeco-inria/gaussian-splatting.git
cd gaussian-splatting
conda env create --file environment.yml
conda activate gaussian_splatting
python train.py -s <path_to_colmap_dataset>
python render.py -m <path_to_trained_model>
```

## Introduction
3D Gaussian Splatting represents scenes using anisotropic 3D Gaussians that are optimized from multi-view images via differentiable rendering. Unlike neural radiance fields that require expensive ray marching, Gaussian splatting uses fast GPU rasterization to achieve real-time rendering while matching or exceeding NeRF quality.

## What 3D Gaussian Splatting Does
- Reconstructs 3D scenes from calibrated multi-view photographs
- Renders novel viewpoints at 30+ FPS at 1080p resolution on consumer GPUs
- Optimizes position, covariance, color, and opacity of millions of 3D Gaussians
- Supports adaptive density control to add or remove Gaussians during training
- Exports trained scenes for real-time interactive viewers

## Architecture Overview
The method initializes sparse 3D Gaussians from Structure-from-Motion point clouds. Each Gaussian stores position, anisotropic covariance (rotation + scale), opacity, and spherical harmonic color coefficients. A tile-based rasterizer projects and alpha-composites sorted Gaussians per pixel. Gradients flow through the differentiable rasterizer to optimize all parameters, while adaptive density control splits, clones, or prunes Gaussians based on view-space gradients and opacity.

## Self-Hosting & Configuration
- Requires COLMAP-processed images (camera poses and sparse point cloud)
- CUDA 11.6+ and a GPU with 12+ GB VRAM for training typical scenes
- Training takes 20-40 minutes on an RTX 3090 for bounded scenes
- Configure iterations, densification intervals, and SH degree in training args
- Viewer application uses OpenGL for real-time scene exploration

## Key Features
- Real-time rendering via efficient tile-based GPU rasterization
- Differentiable rendering enables end-to-end optimization from images
- Adaptive density control automatically refines scene representation
- Anisotropic Gaussians capture both fine detail and smooth surfaces
- Compact representation compared to voxel or MLP-based methods

## Comparison with Similar Tools
- **NeRF (Instant-NGP)** — ray-marching MLP approach; slower rendering but compact model size
- **Nerfstudio** — framework for multiple NeRF methods; Gaussian splatting is faster at inference
- **3DGS variants (Mip-Splatting, SuGaR)** — extensions addressing aliasing or mesh extraction
- **Point-based rendering** — earlier point splatting lacked differentiable optimization
- **Plenoxels** — voxel-based radiance fields; similar speed but more memory

## FAQ
**Q: What input data format is required?**
A: A set of images with camera poses from COLMAP, or any SfM tool that produces a compatible sparse reconstruction.

**Q: How much disk space does a trained model use?**
A: Typically 50-500 MB depending on scene complexity and number of Gaussians.

**Q: Can it handle dynamic scenes?**
A: The base method is for static scenes. Extensions like Dynamic 3D Gaussians add temporal modeling.

**Q: What hardware is needed for real-time viewing?**
A: Any modern GPU supporting OpenGL 4.5 or Vulkan can render trained scenes in real-time.

## Sources
- https://github.com/graphdeco-inria/gaussian-splatting
- https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

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Source: https://tokrepo.com/en/workflows/3d-gaussian-splatting-real-time-radiance-field-rendering-a0d511f2
Author: AI Open Source