Awesome Gaussian Skills
The Most Comprehensive 3D Gaussian Splatting Catalog — 789+ Methods, 25 Categories, Interactive Explorer
You shouldn't search 20 repos for 3DGS papers. This is the only one you need.
Why This Repo?
Other awesome lists give you paper titles. We give you paper titles + an AI toolkit that makes you faster.
| What You Need | Other Lists | This Repo |
|---|---|---|
| Browse papers | Static markdown table | Interactive explorer: search, filter, sort |
| Compare methods | Open 2 papers side by side | 10+ dimension auto-comparison |
| Avoid code bugs | Discover after submission | 108+ known bug pattern detection |
| Design experiments | Guess baselines & ablations | Venue-tailored experiment plan |
| NeRF → 3DGS | Trial-and-error porting | Step-by-step migration guide |
| CAD ↔ 3DGS | No coverage | 61+ method conversion pipeline |
| Patent filing | Manual from scratch | Auto-generated claims & specs |
Live Demo
Try the Interactive Method Explorer →
Search 789+ Methods instantly, filter by category, sort by citations, click any method card for details.
📖 Online Book: Spatial & Embodied Intelligence (New!)
** NEW (Jul 2026)** — A full open-source technical book, built around 3D Gaussian Splatting as the spine and weaving together spatial intelligence and embodied intelligence into one closed loop: representation → perception → planning → action.
Core formula (echoing Agent = LLM + Context + Tools):
Embodied Agent = Spatial Representation × Perception × Planning × Action
What's inside — 12 chapters, every method name anchored to this repo's real data (789+ Methods, 25 categories, 15 skills), zero fabrication:
| # | Chapter | Focus |
|---|---|---|
| 引言 | Why this book | Why 3DGS is the key puzzle piece of Physical AI |
| CH 01 | NeRF → 3DGS: A paradigm leap | Explicit vs implicit, the three innovations, the alpha-compositing formula |
| CH 02 | The math & engineering core | Anisotropic Gaussians, differentiable rasterization, adaptive density control, CUDA |
| CH 03 | From scene to world | Large-scale, dynamic/4D, GS-SLAM, compression & deployment |
| CH 04 | Semantic Gaussians | CLIP/DINO feature distillation, open-vocabulary 3D segmentation |
| CH 05 | Editing · Generation · Asset-ization | Feed-forward reconstruction, SDS generation, animatable assets, PBR relighting |
| CH 06 | Embodied intelligence basics | VLA lineage (RT/π0/GR00T/ReconVLA), simulation, Sim2Real |
| CH 06 | 3DGS as robot spatial memory | GS-SLAM, map-as-renderer, three tiers of spatial memory |
| CH 08 | Object-level & articulated understanding | Part-level Gaussians, URDF bridging, the CAD·Mesh·3DGS triangle |
| CH 09 | Agent-driven digital twins | MCP rendering pipeline, gesture interaction, the perception-action loop |
| CH 10 | World models & the future | Six schools of world models, 3DGS×World Model, spatial foundation models, Physical AI |
| 后记 | Will 3DGS be eaten? | Why explicit representations will be compressed, not consumed |
Each chapter ends with hands-on exercises and links back to the repo's method tables, references/, and skills — so reading the book and doing the engineering are one seamless flow.
Highlights you won't find in a paper list:
- The six schools of world models (2026 taxonomy) and where 3DGS sits as the only representation that is simultaneously renderable, differentiable, and editable.
- How
GS-World,ManiGaussian, andOrbiSimturn 3DGS into a differentiable simulation engine. - A three-tier model of robot spatial memory (geometric → appearance → semantic) and where current GS-SLAM actually stands.
This book is the narrative layer over the repo's data layer. The repo gives you 789+ method names, abstracts, and 14 engineering skills — but not the through-line that connects them. The book supplies that through-line: it argues why 3DGS became the bridge between spatial intelligence and embodied intelligence, and walks every chapter back to concrete methods and skills you can use today. Read the book to understand the map; use the repo to ship the territory.
What's New (July 2026)
Latest update (Jul 26): v0.5.1 — Full Method Audit & 14 New Methods. Now 789+ Methods (775 verified unique baseline + 14 new). Full re-audit across 11 source files; all method counts unified to 789+. New additions: GrainGS (dynamic, 36.98 dB / 435.6 FPS / 4.67 MB), GLAM-SLAM (IROS 2026, outdoor decoupled SLAM), SubSplat (subpixel feed-forward), ATSplat (adaptive 3D tokens, 1136 FPS), 3D-GIMP (3DGS inpainting), LB-Edit (7× lower editing latency), FlexiAvatar (ECCV 2026, visible-body-only optimization), ZeroSplat (ECCV 2026, training-free segmentation), CaT-GS (CVPR 2026, 10× faster rendering), FF-ProCams (projector-camera inverse rendering), i3dgs (SIGGRAPH 2026, large-scale unordered), VIGS-SLAM (ECCV 2026, iPhone real-time), ECoNGS (IEEE VIS 2026, volume visualization), AniGS (scene-level animation via diffusion prior). +MoDE/MoE-GS code link. Previous (Jul 24): v0.5.0 MCP Protocol Implementation. Previous (Jul 23): v0.4.3 ICML 2026 & Material/Provenance Wave — GaussTrace (ICML 2026), GADA (ICML 2026), InvSplat, MGM, DualPhys-GS, StereoGS. v0.4.4 added 3dgs-training-debugger skill (60+ runtime patterns).
| Method | Venue | Category | One-Line Innovation |
|---|---|---|---|
| Proxy-GS | CVPR 2026 Oral | Acceleration | Lightweight proxy model for 2.5x speedup with no accuracy loss |
| Z-Order GS | CVPR 2026 Oral | Feed-Forward | Z-order Morton curve + sparse attention O(N²)→O(N log N) |
| 3DReflecNet | CVPR 2026 Best Paper Candidate | Cross-Domain | 120K+ objects, 48 material combos, 3 failure modes |
| Flux-GS | ECCV 2026 | Acceleration | Flux-based Gaussian splatting for real-time rendering |
| AnchorSplat | ECCV 2026 | Optimization | Anchor-driven splatting with efficient density control |
| ASSEMCAD | ECCV 2026 | CAD | Assembly-aware CAD reconstruction from 3DGS |
| WildSplat | ECCV 2026 | Robustness | In-the-wild scene reconstruction with transient object removal |
| NoDrift3R | ECCV 2026 | SLAM | Drift-free dense 3D reconstruction via point map regression |
| Axis-Shared Rasterization Accelerator | ISCA 2026 | Acceleration | Hardware accelerator with axis-shared tiled rasterization |
| Prune Wisely | — | Optimization | 90% Gaussian pruning via DoG importance criterion |
| Provable Pruning via Coresets | — | Optimization | Coreset-based provable Gaussian pruning with bounded error |
| StreamLoD-GS | — | Streaming | LoD-based progressive streaming with view-dependent quality |
| CADDreamer | CVPR 2025 Highlight | CAD | Text/sketch → CAD B-rep generation |
| GaussTrace | ICML 2026 | Security | 3DGS provenance analysis via LLM reasoning for IP forensics |
| GADA | ICML 2026 | Feed-Forward | Geometry-aware deformable aggregation, 2.13× faster FPS |
| InvSplat | arXiv 2026 | Feed-Forward | Inverse feed-forward splatting with intrinsic PBR materials |
| MGM | arXiv 2026 | Relighting | Large material Gaussian model for relightable 3D generation |
| DualPhys-GS | arXiv 2026 | Robustness | Dual physics-guided 3DGS for underwater reconstruction |
| StereoGS | 2026 | Acceleration | Energy-efficient hardware stereoscopic GS rendering processor |
Full changelog: changelog/
Quick Start
Each skill is a standalone SKILL.md file — copy it to your Agent's skills directory.
3 commands to your first AI-powered 3DGS workflow:
git clone https://github.com/jaccen/Awesome-Gaussian-Skills.git
# Option 1: Claude Code
cp -r Awesome-Gaussian-Skills/skills/* .claude/
# Option 2: Cursor
cp -r Awesome-Gaussian-Skills/skills/* .cursor/rules/
# Option 3: One-Click Install
curl -sSL https://raw.githubusercontent.com/jaccen/Awesome-Gaussian-Skills/main/scripts/setup.sh | bash
Then ask your Agent: "Compare 3DGS and 2DGS rendering formulations"
Knowledge Base (789+ Methods, 25 Categories)
| Group | Categories | Key Topics |
|---|---|---|
| Core Representations | Foundation, Antialiasing, Optimization, Surface/Rendering, Image Rep. | 3DGS, 2DGS, Scaffold-GS, Mip-Splatting, GaussianImage |
| Efficiency & Scale | Compre |