awesome-gaussian-skills

Awesome Gaussian Skills

The Most Comprehensive 3D Gaussian Splatting Catalog — 976 methods, 26 Categories, Interactive Explorer

You shouldn't search 20 repos for 3DGS papers. This is the only one you need.

English | 中文

• Why This Repo?
• Live Demo
• Quick Start
• Integrations
• Online Book
• What's New (Oct 2026)
• Knowledge Base
• 18 AI-Powered Skills
• Visualization Samples
• Research Innovation Highlights
• Roadmap
• Architecture
• SplatVerse Studio
• Contributing
• Citation
• Ecosystem & Acknowledgments
• License
• Sponsor & Community
• Star History

Why This Repo?

Other awesome lists give you paper titles. We give you paper titles + an AI toolkit that makes you faster. The canonical lists catalog the ecosystem for humans; this repo makes it consumable by AI agents — let your AI read the papers for you.

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	104 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	40+ method conversion pipeline
Patent filing	Manual from scratch	Auto-generated claims & specs

Live Demo

Try the Interactive Method Explorer →

Search 976 Methods instantly, filter by category, sort by citations, click any method card for details.

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"

Integrations

This repo ships as both AI agent skills and an MCP server, so it works across agent runtimes and MCP clients — the knowledge base is consumable by your AI agent, not just by you:

Client	How to use
Claude Code / OpenClaw	/plugin marketplace add jaccen/Awesome-Gaussian-Skills → install gaussian-research, gaussian-engineering, or gaussian-domain (18 skills in 3 plugins); or load skills/ directly (see CLAUDE.md); MCP server via the config below
DSH (DeepSeek Harness)	dsh plugin --profile web add jaccen/Awesome-Gaussian-Skills — the 18 skills + MCP renderer auto-mount via cordis.patch.yml
Claude Desktop / Cursor / Cline	copy the MCP stdio config from .marketplace/mcp-servers/client-configs.md
Any MCP client	command: node, args: [/path/to/mcp-server/dist/index.js], transport: stdio — 34 tools (21 core + 13 experimental)

The MCP server exposes 34 tools — 21 core (import_scene, set_camera, modify_gaussians, render_frame, sculpt_pipeline, SLAT latent editing, …) + 13 experimental stubs — see mcp-server/README.md and .marketplace/mcp-servers/mcp-metadata.json.

📖 Online Book: Spatial & Embodied Intelligence

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.

📖 Read the Book →

Core formula (echoing Agent = LLM + Context + Tools):

Embodied Agent = Spatial Representation × Perception × Planning × Action

#	Chapter	Focus
引言	Why this book	Why 3DGS is the key puzzle piece of Physical AI
前	Landing prelude: should you, can you	Tech-selection matrix, eight cases where you should NOT use 3DGS, three reading paths
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
操	Data-capture SOP	Device selection, trajectory design, seven exposure hard rules, scale calibration —the first step that decides your quality ceiling
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 07	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
CH 11	Security, provenance & copyright	3DGS watermarking, GaussTrace forensics, adversarial attacks, IP management
CH 12	Capture-to-delivery: the end-to-end pipeline	Ten-stage tool selection & pitfalls, formats & coordinate systems, cost/duration estimates, project failure modes
CH 13	Industry playbooks: six verticals	Museums & tourism, e-commerce, industrial inspection, AD simulation, embodied robots, surveying & mapping
附	Appendix	Datasets, evaluation metrics, delivery-metric conversion & acceptance-report templates
实	Practice index: from book to engineering	18 engineering skills · 34 MCP tools · 976+ method explorer, chapter-to-runnable-resource mapping
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, and OrbiSim turn 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 976 method names, abstracts, and 18 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 (Oct 2026)

Latest Update (Oct 19, 2026): v0.9.12 — Full-Library Audit + arXiv Scan: 21 New Methods (955 → 976) ⭐

Full-library audit plus a fresh arXiv scan admitted 21 new arXiv-verified 3DGS methods, taking the library from 955 to 976 (+21, +2.2%) across 8 categories — led by Compression & Streaming (+5), Cross-Domain (+3), Autonomous Driving (+2), World Models & Spatial Intelligence (+2), SLAM (+2). Every admitted method name is self-stated in its own paper's title/abstract (R9 protocol) and arXiv-verified; 13 candidates were rejected (no self-stated method name, survey, NeRF, or 3DGS used merely as a tool); one name collision (X-Splat) kept the existing entry and dropped the newcomer.

Audit fixes: one mis-attached description (STC-GS — corrected to its real contribution, a spatio-temporal coherent GS for weather-radar sequence prediction) and 16 blank descriptions backfilled. Full arXiv-ID re-verification across 1,462 unique IDs (1,461 reachable; the single unresolved ID was a historical note rather than a live record). All data carriers (methods.json / CSV / methods.html / abstracts.js / reference docs) re-verified at 976, name sets identical. See changelog.

Previous Update (Oct 15, 2026): v0.9.11 — Research Expert Team Deep Expansion: Methodology Core + 15 Master-Paper Studies ⭐

Pure skill-content release — no carrier changes (955 methods / 26 categories / 18 skills stay). research-expert-team grew from a nine-role router into a two-layer methodology asset:

• Methodology core (program layer) — executable procedures distilled from 15 top-cited exemplars: topic-selection engine (M1-M4 + five opening-sentence variants), argumentation skeletons (T1-T3 + representation-paper techniques B.5), 16 experiment patterns (C1-C16), eight reviewer must-ask questions with preempts (D1-D8), nine value amplifiers.
• Master-paper evidence base (5 directions, 15 papers) — full-text dissections of Kimera, ConceptGraphs, ReAct, MetaGPT, Toolformer, Generative Agents, Voyager, DreamFusion, DreamGaussian, 3DGS, 4DGS, SuGaR, NeRF, Instant-NGP, DUSt3R (citations verified via Semantic Scholar).