# graphdeco-inria/gaussian-splatting

Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

Repository: https://github.com/graphdeco-inria/gaussian-splatting
Canonical: https://ross.abutalabs.com/products/gaussian-splatting
Homepage: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
Language: Python
License: NOASSERTION
License Family: other
Topics: computer-graphics, computer-vision, radiance-field
Last push: 2025-10-17T14:28:15+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 47, release rhythm 35, longevity 82
- inputs: {"age_days": 1156, "days_push": 320, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 23425, forks 3370 (observed 2026-08-28T04:11:34.753805+00:00)

## What it is
The official reference implementation of 3D Gaussian Splatting, a method for real-time radiance field rendering that reconstructs scenes from photos into 3D Gaussians. It supports training from calibrated image sets and real-time (≥30 fps) 1080p novel-view synthesis.

## Use cases
- reconstruct 3D scenes from photos
- render novel views of a captured scene in real time
- train a radiance field model on my own image dataset
- reproduce results from the 3D Gaussian Splatting paper
- build 3D Gaussian splatting research on top of a reference codebase

## When to choose
- you want the canonical, paper-faithful implementation of 3D Gaussian Splatting
- you need high-quality real-time novel-view synthesis at 1080p
- you have GPU resources and calibrated multi-view images
- you are doing graphics or vision research on radiance fields

## When to avoid
- you need a permissively licensed library for commercial products (license is custom/non-standard)
- you have no CUDA-capable GPU
- you want a polished end-user photogrammetry application rather than research code
- you need simple object scanning with minimal setup

## Facets
- artifact type: library
- maturity: active
- function: graphics, computer-vision, machine-learning, simulation
- domain: computer-vision, graphics, machine-learning
- platform: python, windows
- tags: gaussian-splatting, novel-view-synthesis, radiance-fields, 3d-reconstruction, neural-rendering, reference-implementation, linux, gpu

## Member repositories
- graphdeco-inria/gaussian-splatting (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:34.753805+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:56:28.573444+00:00, confidence not recorded.
  - readme: https://github.com/graphdeco-inria/gaussian-splatting (fetched 2026-08-28T04:11:34.753805+00:00, sha 77032b32cb4b)
  - homepage: https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ (fetched 2026-08-29T07:54:22.632811+00:00, sha 5f1b798fdec1)
- Data as of 2026-08-30T08:39:29.467469+00:00.