Anttwo/SuGaR

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3495 stars · 306 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SuGaR is the official PyTorch implementation of a CVPR 2024 method that extracts accurate, editable meshes from 3D Gaussian Splatting reconstructions within minutes on a single GPU. It binds Gaussians to mesh surfaces, enabling editing, animation, and relighting in tools like Blender, Unity, and Unreal while preserving high-quality Gaussian Splatting rendering.

Use cases

• extract a mesh from 3D gaussian splatting scenes
• reconstruct editable 3D surfaces from photos
• animate gaussian splatting scenes in blender
• convert 3DGS reconstructions to textured meshes
• edit and rig gaussian splatting scenes via a mesh
• fast mesh extraction from neural radiance representations

When to choose

• you need editable meshes from 3D Gaussian Splatting reconstructions quickly
• you want to animate or composite Gaussian Splatting scenes in Blender, Unity, or Unreal
• you need faster mesh extraction than neural SDF methods with better rendering quality

When to avoid

• you only need novel-view rendering without meshes, where vanilla 3D Gaussian Splatting suffices
• you lack a CUDA-capable GPU
• you need a permissively licensed library for commercial redistribution, since the license is non-standard

Facets

library · maturity active

machine-learning graphics image-processing simulation computer-vision graphics machine-learning deep-learning python cpp 3d-gaussian-splatting mesh-reconstruction surface-reconstruction neural-rendering nerf cvpr-2024 pytorch 3d-reconstruction blender-addon gpu linux

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