# graphdeco-inria/diff-gaussian-rasterization

Repository: https://github.com/graphdeco-inria/diff-gaussian-rasterization
Canonical: https://ross.abutalabs.com/products/diff-gaussian-rasterization
Language: Cuda
License: NOASSERTION
License Family: other
Last push: 2024-10-21T07:24:49+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 82
- inputs: {"age_days": 1156, "days_push": 681, "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 1489, forks 481 (observed 2026-08-28T04:04:52.307581+00:00)

## What it is
A CUDA-based differentiable rasterization engine for 3D Gaussian Splatting, used in the SIGGRAPH 2023 paper '3D Gaussian Splatting for Real-Time Rendering of Radiance Fields'. It enables gradient-based optimization of Gaussian primitives for real-time radiance field rendering.

## Use cases
- differentiable rasterizer for 3D gaussian splatting training
- render radiance fields in real time
- train novel view synthesis models with gradients through rasterization
- research implementation of gaussian splatting paper
- cuda rasterization engine for graphics research

## When to choose
- you are training or extending 3D Gaussian Splatting models
- you need a differentiable rasterization pipeline on CUDA GPUs
- you are doing academic research on radiance fields and novel view synthesis

## When to avoid
- you need a general-purpose production renderer
- you have no CUDA-capable GPU
- you need a permissively licensed library for commercial products (license is non-standard)
- you want a ready-to-use application rather than a research component

## Facets
- artifact type: library
- maturity: stable
- function: graphics, machine-learning, gpu-computing
- domain: computer-vision, graphics, deep-learning
- platform: cpp, python
- tags: gaussian-splatting, cuda, differentiable-rendering, neural-rendering, radiance-fields, research-code, gpu, linux

## Member repositories
- graphdeco-inria/diff-gaussian-rasterization (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:52.307581+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 2026-08-30T04:33:41.088074+00:00, confidence not recorded.
  - readme: https://github.com/graphdeco-inria/diff-gaussian-rasterization (fetched 2026-08-28T04:04:52.307581+00:00, sha c82413c3a880)
- Data as of 2026-08-30T08:39:29.467469+00:00.