awesome-gaussian-skills

Awesome Gaussian Skills

The Most Comprehensive 3D Gaussian Splatting Catalog — 900 methods, 23 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?

Other awesome lists give you paper titles. We give you paper titles + an AI toolkit that makes you faster.

Live Demo

Try the Interactive Method Explorer →

Search 900 Methods instantly, filter by category, sort by citations, click any method card for details.

Integrations

This repo ships as both AI agent skills and an MCP server, so it works across agent runtimes and MCP clients:

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

📖 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.

📖 Read the Book →

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

Embodied Agent = Spatial Representation × Perception × Planning × Action

What's inside — 13 chapters + hands-on prelude & capture SOP, every method name anchored to this repo's real data (900 Methods, 23 categories, 15 skills), zero fabrication:

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.

What's New (Oct 2026)

Latest Update (Oct 20, 2026): v0.9.8 — World Models Category Reinforcement (896 → 900) ⭐

Targeted reinforcement of the thinnest category. 4 new arXiv-verified 3DGS-based World Model / Spatial Intelligence methods admitted, taking the library from 896 to 900. The World Models & Spatial Intelligence category grew from 8 → 12 methods: HY-World 2.0, F3DGS (federated multi-agent world modeling), 4DGS-WAM (object-centric world action model), Cloth-Splatting (3DGS cloth state estimation). Two missing arXiv IDs backfilled (Holi-Spatial → 2603.07660, Spatial-TTT → 2603.12255); two entries flagged unverified pending source check (APEIRIA, S²AM3D). GSMem found already in library under Embodied AI & Robotics — not duplicated. See changelog.

Previous Update (Oct 12, 2026): v0.9.7 — Manual Admission: 15 New Methods (881 → 896) ⭐

Manual admission of 15 new arXiv-verified 3DGS methods, taking the library from 881 to 896 (+15, +1.7%). Admissions span 9 categories: Acceleration (H2-Ray, TileSkipper, SplatQ), Feed-Forward (CompactSplat, DeltaSplat, DensiTok), Embodied AI & Robotics (Splat2Act, 2DGS-Planner, NovaSplat), plus OuroWorld (Dynamic & 4D), PAM-ToD (HDR & Relighting), FlyMark (Security), GSCV (Compression & Streaming), OpenSplatGraph (Language & Semantic), PCAsplat (Surface & Rendering). Every admitted method name is self-stated in its own paper's title/abstract (R9 protocol) and arXiv-verified. 12 candidate papers were rejected for lacking a self-stated method name, being benchmark-only, NeRF (not 3DGS), or using 3DGS merely as a tool. All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 896, name sets compared entry by entry. No method name was fabricated.

See changelog/2026-10-12.md for the full admission list.

Update (Oct 7, 2026): v0.9.6 — Manual Admission: 9 New Methods (872 → 881) ⭐

Manual admission of 9 new arXiv-verified 3DGS methods, taking the library from 872 to 881 (+9, +1.0%). Admission includes Speedy-Splat (CVPR 2025, precise tile intersection + efficient Hessian-based pruning, 6.71× render speedup) and 8 recent September 2026 papers: AESplat (pose-free feed-forward), AGILE-GS (anchor-guided NBV), OC-GS (irregular turntable), ClearGS (handheld video), ChronoFuseGS (multi-temporal fusion, PG 2026), TangoGS (cross-scale compact), RRTO-CF3DGS (COLMAP-free), Observation-Gram (SH compression). All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 881, name sets compared entry by entry. No method name was fabricated.

See changelog/2026-10-07.md for the full admission list.

Update (Sep 21, 2026): v0.9.5 — Daily arXiv Scan: 13 New Methods (859 → 872) ⭐

Routine daily arXiv scan admitted 13 new arXiv-verified 3DGS methods, taking the library from 859 to 872 (+13, +1.5%). Admissions span Surface & Rendering, Feed-Forward, Large-Scale, Compression, Sparse-View, Cross-Domain, Embodied AI & Robotics, Dynamic, and Language & Semantic categories (incl. LiteTex-GS, GrapeSplat, VDGS, CRP-GS, NormLift). 7 candidate papers were rejected for failing the verification protocol — benchmarks without a self-stated method name, descriptive-only paper titles, or 3DGS used merely as a tool. All data carriers (methods.json / CSV / methods.html / abstracts.js / reference doc) re-verified at 872, name sets compared entry by entry. No method name was fabricated; the library's "every entry arXiv-verifiable" guarantee holds.

See changelog/2026-09-21-v3.md for the full admission list.

^{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 (900 Methods, 23 Categories)

Download full database: CSV | Full analysis: references/3dgs-methods-overview.md

15 AI-Powered Skills

Works with Claude Code, Cursor, Windsurf, and other AI Agent frameworks.

Visualization Samples

Generated by 3dgs-visualizer —see Test/ for full-resolution files.

Radar Chart	Metrics Bar Chart

Quality vs Speed	Metrics Heatmap

Research Innovation Highlights

Derived from systematic gap analysis across 900 Methods. Target venues: TVCG / CGF / CAD / T-RO / IJCV / ACM TOG.

Roadmap

• v0.1 —Initial release with 6 core skills (Apr 2026)
• v0.2 —3dgs-visualizer + Text2Word demo (May 2026)
• v0.3 —Knowledge base 665->789+ Methods, 25 Categories, 101+ bug patterns, 12 skills (Jun 2026)
• v0.3.6 —Spatial intelligence wave: 680->639+ methods, +10 new methods (FastGS, Holi-Spatial, Spatial-TTT, etc.), Dimension 11, Anthropic standard alignment (Jun 25, 2026)
• v0.3.6 —CVPR 2026 representative papers: 690->639+ methods, +23 verified new methods, all 13 skills updated (Jun 28, 2026)
• v0.4.0 —Router Architecture Expansion: cg-paper-writing + 3dgs-engineering-guide → Router + manifest.yaml + static/; 3dgs-code-reviewer Self-Check Loop; cg-paper-writing Stage Gates; 3 Router skills total (Jul 2, 2026)
• v0.4.1 —ECCV & ISCA 2026 Wave: +Flux-GS, AnchorSplat, ASSEMCAD, WildSplat, NoDrift3R (ECCV 2026), Axis-Shared Rasterization Accelerator (ISCA 2026), Provable Pruning via Coresets; 639+ methods (Jul 9, 2026)
• v0.4.2 —SIGGRAPH & MICCAI 2026 Wave: +DP-Splat, MoE-GS/MoDE (TPAMI 2026), HyperGS, MAC-Splat (ECCV 2026), Track2Map (MICCAI 2026), PEAR (SIGGRAPH 2026), CoSAG, HoloTetSphere (ECCV 2026), SalientGS; 639→ 89+ Methods, +3dgs-compression-deploy skill, 14 skills total (Jul 14, 2026)
• v0.4.3 —ICML 2026 & Material/Provenance Wave: +GaussTrace (ICML 2026), GADA (ICML 2026), InvSplat, MGM, DualPhys-GS, StereoGS; 660→ 89+ Methods, +3 bug patterns (108+ total), MCP roadmap v0.2.3 (Jul 23, 2026)
• v0.4.4 —Training Debugger Skill: +1 skill (3dgs-training-debugger, 60+ runtime patterns, VRAM management, convergence analysis), 14→ 5 skills total (Jul 23, 2026)
• v0.4 —3dgs-spatial-agent enhancements (knowledge-constrained CAD, DDF-GS ray query)
• v0.5.0 —MCP Protocol Implementation: 24-tool MCP server (mcp-server/), Three.js WebSocket renderer, 24-pattern voice intent mapper, headless mode, voice demo (Jul 24, 2026)
• v0.5.1 —Full Method Audit & 14 New Methods: 775 verified unique baseline + 14 new = 789+ methods; all method counts unified across 11 source files; +GrainGS, GLAM-SLAM, SubSplat, ATSplat, 3D-GIMP, LB-Edit, FlexiAvatar, ZeroSplat, CaT-GS, FF-ProCams, i3dgs, VIGS-SLAM, ECoNGS, AniGS (Jul 26, 2026)
• v0.8.0 —Platform Upgrade (P0+P1+P2): single source of truth (data/methods.json, 783 methods, 23 categories, data CI); 5 fabricated entries purged + 14 arXiv-verified frontier methods; true-3DGS render loop (gsplat via HTTP-served PLY); server-authoritative scene persistence; real PLY/SPLAT export; 5 prune strategies; grid-accelerated ray query; runtime arg validation; WS origin allowlist; 21 unit tests + 2 CI workflows; Benchmark arena (bench/); skill orchestration contracts (skills/_contracts/); Router manifest loader (scripts/router_load.py); 13 core MCP tools + 13 experimental (Aug 7, 2026)
• v0.8.1 —Daily Update: 7 new arXiv-verified methods (LEGO, OutLangSplat, ESVR, TRACE-GS, RORA, OVOW, Super-Gaussian); 783→ 90 methods; 6 categories updated; all data carriers in sync (Aug 23, 2026)
• v0.8.2 —Daily Update: 11 new arXiv-verified methods (BRF-GS, SMG, VCAR, RealCAD, ObjectSplat, CapFrame, Amortized Anchor Refinement, ATGS, CausalSplat, VoroTracing, Lucida); 790→ 01 methods; 7 categories updated; all data carriers in sync (Sep 2, 2026)
• v0.8.3 —Dynamic Scene Survey Integration: 23 new arXiv-verified methods from 连振晗 et al. survey (J CAD & CG, Jan 2026); 801→ 19 methods; Dynamic & 4D 75→ 7; +dynamic-datasets.md (11 datasets); +benchmark-data.md metrics (MS-SSIM/VMAF/FID) and 4 performance tables; experiment-planner skill updated (Sep 4, 2026)
• v0.9.0 —Data Accuracy Release: full-library name↔ paper verification against arXiv API; 181 unverifiable entries removed, 56 wrong IDs fixed, 5 renames, 1 dedup; count corrected to 678 (Sep 16, 2026)
• v0.8.4 —Daily Update: 39 new arXiv-verified methods (arXiv window 2026-09-02 ~ 09-16); 819→ 78 methods; 15 categories updated (Compression & Streaming +4, Large-Scale +4, Optimization +4, Robustness +4, Cross-Domain +3, Dynamic & 4D +3, Generation +3, SLAM +3, Acceleration +2, Embodied AI & Robotics +2, Feed-Forward +2, Sparse-View +2, HDR & Relighting +1, Human & Avatar +1, Surface & Rendering +1); pure benchmarks/surveys excluded; validate_knowledge_base.py SyntaxWarning fixed; all data carriers in sync (Sep 16, 2026)
• v0.9.1 —ECCV 2026 Wave: 14 new arXiv-verified methods (Wat3R, GeoNVS, Edit3r, SA-ResGS, GaussianLens, Deformable Triangle Splatting, Neural Harmonic Textures, CubicSplat, TetraSDF, Heat Kernel Textures, GRF-Recon, PriSplat, Per-View Distractor Filtering, GS-Underwater); 678→ 92 methods; 6 categories updated (Surface & Rendering +5, Feed-Forward +4, Cross-Domain +2, Robustness +2, Editing +1, Embodied AI & Robotics +1); all data carriers in sync (Sep 20, 2026)
• v0.9.2 —Scene Agent + Mapmost Engineering Wave: 5 new scene-generation Agent methods (SceneSmith ICML 2026, Scenethesis ICLR 2026, SceneGenAgent ACL 2025, SceneConductor, SceneAssistant); 692→ 97 methods; Generation +4, Embodied AI & Robotics +1; 6 skill file enhancements: 3DGS individualization, LOD streaming + seam-fix, adaptive partition training, 3DGS+3DTiles fusion, LUT color grading, Mapmost 100km² benchmark (Sep 20, 2026)
• v1.0 —CI/CD integration + multi-framework official listings
• v2.0 —Agent-to-Agent collaboration (multi-agent paper discussion)

^{Full version history: changelog/}

Architecture

Awesome-Gaussian-Skills/
├── data/                      # Single source of truth (methods.json, categories.json)
├── skills/                    # 15 AI Agent skills (SKILL.md format)
│   ├── _contracts/            # Inter-skill I/O schemas (paper-insight, comparison-report, experiment-plan)
│   ├── 3dgs-paper-reader/     # Paper reading & summarization
│   ├── 3dgs-method-compare/   # Method comparison engine (Router)
│   ├── 3dgs-code-reviewer/    # Code review (104 bug patterns)
│   ├── 3dgs-experiment-planner/ # Experiment design
│   ├── nerf-to-3dgs-migrator/ # NeRF→3DGS migration
│   ├── cad-mesh-3dgs/         # CAD/Mesh/3DGS bridge
│   ├── cg-paper-writing/      # CG paper writing assistant (Router)
│   ├── 3dgs-visualizer/       # Research visualization
│   ├── 3dgs-engineering-guide/ # Engineering deployment (Router)
│   ├── patent-software-ip/    # Patent & copyright generation
│   ├── 3dgs-spatial-agent/    # Spatial intelligence agent
│   ├── 3dgs-mcp-renderer/     # MCP rendering bridge
│   ├── 3dgs-articulated-reasoner/ # Articulated reasoning & digital twin
│   ├── 3dgs-compression-deploy/  # Compression & deployment
│   └── 3dgs-training-debugger/  # Training failure diagnosis
├── mcp-server/                # MCP server v0.8.0 (13 core + 13 experimental tools, gsplat render loop, HTTP+WS :9842)
├── bench/                     # Benchmark arena (metrics.py, run_eval.py, leaderboard.json)
├── scripts/                   # build_knowledge_base.py, validate_knowledge_base.py, router_load.py, validate_skill_contract.py
├── studio/                    # SplatVerse Studio (bridge + web)
├── docs/                      # GitHub Pages interactive explorer
├── references/                # Knowledge base (900 Methods, 23 Categories)
├── Test/                      # Visualization samples
├── changelog/                 # Version history
└── assets/                    # Project images

Each skill follows the SKILL.md standard, compatible with Claude Code (.claude/), Cursor (.cursor/rules/), Windsurf, and other AI Agent frameworks.

SplatVerse Studio: Short Video Creation

SplatVerse Studio integrates 3D Gaussian Splatting with a short-drama pipeline powered by the Toonflow engine, letting you go from text scripts to 3DGS-rendered video scenes.

Architecture

Toonflow Engine (:10588)                    SplatVerse Studio
+------------------------------+            +------------------------------------------+
| Script -> Assets ->           |   REST     | Bridge (:10590)                           |
| Storyboard -> Video           |<--------->| Project Browser                            |
| Vendor: 3dgs-renderer         |            | Render Studio                              |
|                               |            | MCP Tools (25 tools)                       |
|                               |            | MCP Renderer (:9842)                       |
|                               |            | Pipeline (7 steps)                         |
|                               |            |   |-- Script Adaptation                     |
| MoneyPrinterTurbo (:8081)     |   REST     |   |-- Storyboard                            |
| Online material ->            |<--------->|   |-- Toonflow Sync                         |
| TTS -> FFmpeg -> Video        |            |   |-- TTS Dubbing                           |
| Cross-platform post           |            |   |-- Video Gen                             |
|                               |            |   |-- FFmpeg Compose                        |
| Studio Web (:5173)            |            |   +-- Publish (MPT)                         |
+------------------------------+            +------------------------------------------+

Quick Start

Prerequisites: Node.js ≥18 (Toonflow and MoneyPrinterTurbo are optional).

The one-click launcher starts everything automatically on first run: checks Node.js, creates .env from .env.example if missing, runs npm install if dependencies are missing, builds the MCP Server / Bridge binaries if needed, then starts Toonflow Studio (:10588) (if found), MCP 3D Renderer (:9842), Bridge Server (:10590) and Studio Web (:5173), health-checks every service, and opens your browser.

Option A —Windows (double-click)

Download the project, then double-click start-all.bat in the project root. That's it —all services start automatically and a browser opens at http://localhost:5173.

Option B —npm (any OS)

npm run start:all

Option C —PowerShell (Windows)

powershell -NoProfile -ExecutionPolicy Bypass -File scripts/start-all.ps1

One-time manual step only needed for video generation: set LLM_API_KEY in the generated .env (used for script/storyboard generation).

Toonflow (optional, for the full video pipeline) is auto-detected from ../AI应用/Toonflow-app, ../Toonflow-app, ./Toonflow-app, or TOONFLOW_APP_DIR. MoneyPrinterTurbo (optional, needs Docker) is started only when MPT_ENABLED=true in .env.

Useful flags: -NoToonflow skip Toonflow, -NoMpt skip MPT, -NoBrowser skip auto-opening the browser, -SkipInstall skip npm install.

Minimal (no Toonflow)

If you only need the 3DGS rendering tools without Toonflow:

npm run dev    # MCP + Bridge + Web, no Toonflow

Creating a Short Video: Step by Step

Step 1 —Write a Script in Toonflow

Open the Toonflow web app (typically at http://localhost:10588). Create a project, write a script, and generate storyboards. Each storyboard is a scene with:

• Prompt —text description for image/video generation
• Duration —scene length in seconds
• Track —scene grouping label

Toonflow's pipeline: Text → Script → Assets (roles, scenes, props) → Storyboards → Video

Step 2 —Browse Projects in Studio Web

Open http://localhost:5173 and navigate to Projects. You'll see all Toonflow projects. Click a project to view its storyboards.

# Login to Toonflow
TOKEN=$(curl -s http://localhost:10588/api/login/login \
  -H "Content-Type: application/json" \
  -d '{"username":"admin","password":"admin123"}' | jq -r '.data.token')

# Create a script
curl -s http://localhost:10588/api/script/addScript \
  -H "Authorization: $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"name":"Forest Adventure","content":"# Forest Adventure\n## Scene 1...","projectId":1}'

# Add storyboards
curl -s http://localhost:10588/api/production/storyboard/batchAddStoryboardInfo \
  -H "Authorization: $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "scriptId": 1,
    "projectId": 1,
    "data": [
      {
        "prompt": "A misty forest at dawn, sunlight through trees",
        "videoDesc": "Dawn mist over ancient forest",
        "duration": 5,
        "track": "Scene 1",
        "state": "pending",
        "src": "",
        "shouldGenerateImage": 1,
        "associateAssetsIds": []
      }
    ]
  }'

Step 3 —Render Storyboards as 3DGS Scenes

Two rendering modes in Render Studio (/render):

For batch rendering, enter the Toonflow Project ID and Storyboard IDs (comma-separated), then click Render Batch. The Bridge fetches storyboards from Toonflow, builds 3DGS scenes via MCP Server, and renders each storyboard frame.

Step 4 —Monitor Render Progress

Render progress streams via SSE (Server-Sent Events). You'll see toast notifications in the top-right corner as each storyboard renders. The Dashboard shows recent render tasks with progress bars.

Step 5 — 3DGS as a Toonflow Vendor (Optional)

To use 3DGS rendering directly inside Toonflow's image/video generation:

# Copy the vendor adapter to Toonflow
cp studio/bridge/vendor/3dgs-renderer.ts ../AI应用/Toonflow-app/data/vendor/

This registers 3DGS as both an image model (single-frame render) and video model (multi-frame animation) in Toonflow's vendor system.

Port Reference

Troubleshooting

• "Toonflow engine not running" —Start Toonflow: npm run start:all or manually node data/serve/app.js in the Toonflow directory
• Projects page shows no storyboards —Create a script first in Toonflow; storyboards belong to scripts
• 3DGS vendor not available in Toonflow —Copy studio/bridge/vendor/3dgs-renderer.ts to Toonflow's data/vendor/ directory

MoneyPrinterTurbo (MPT) Integration

MoneyPrinterTurbo is integrated as an optional HTTP API sidecar, extending SplatVerse Studio with online material video generation, additional TTS voices, and cross-platform publishing. Zero intrusion —when MPT_ENABLED=false or MPT_API_URL is unset, the pipeline behaves exactly as before.

What MPT Adds

Frontend Entry

The Studio Web "Script → Video" page (/pipeline) ships a built-in MPT entry:

• Config panel: a "📖 MoneyPrinterTurbo Integration" group at the bottom of the model config section —enable toggle, service URL, material source, default voice, plus a live connection status (Connected / Not connected). Saving writes the settings into .env.
• Per-task options: a "📖 MoneyPrinterTurbo Fallback" block at the bottom of the input section —check "Video fallback" and "TTS fallback" to automatically degrade to MPT when any main-pipeline step (3DGS / Toonflow / TTS) fails; when MPT is enabled you can also pick an MPT voice and publish platforms (TikTok / YouTube / Instagram).

Note: the bridge auto-loads the project-root .env at startup (dependency-free implementation, see studio/bridge/src/load-env.ts), so config saved from the UI takes effect after the bridge restarts. Launch the bridge from the project root (npm run dev:bridge or scripts/start-dev.ps1) to ensure the root .env is picked up.

Setup

# 1. Configure MPT (fill in at least one material API key)
cp mpt-config.example.toml mpt-config.toml
#    Edit mpt-config.toml —required: pexels.api_key or pixabay.api_key (both free)

# 2. Start MPT container
docker compose -f docker-compose.mpt.yml up -d

# 3. Verify MPT is running
curl http://localhost:8081/api/v1/tasks?page=1&page_size=1

# 4. Enable MPT in Studio .env
#    MPT_ENABLED=true
#    MPT_API_URL=http://localhost:8081

Configuration Reference

MPT-side settings (API keys for Pexels/Pixabay, TTS providers, LLM) go in mpt-config.toml, not .env. See mpt-config.example.toml for the full template.

API Endpoints

Creating a Task with MPT Fallback

curl -s http://localhost:10590/api/pipeline/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "text": "In a quiet town, a kitten named HuaHua chats with a butterfly...",
    "title": "HuaHua Adventure",
    "style": "水彩",
    "videoRatio": "16:9",
    "enableTTS": true,
    "enableVideoGen": false,
    "enableMptFallback": true,
    "enableMptTTS": true,
    "mptVoiceName": "zh-CN-XiaoxiaoNeural",
    "publishPlatforms": [
      { "name": "tiktok", "title": "HuaHua Adventure", "tags": ["animation", "cat"] }
    ]
  }'

The 7-step pipeline: Script Adaptation → Storyboard → Toonflow Sync → TTS (MPT fallback) → Video Gen → FFmpeg Compose (MPT fallback) → Publish (MPT)

Contributing

Contributions welcome! See Contributing Guide.

Citation

@misc{awesome-gaussian-skills,
  author = {jaccen},
  title = {Awesome Gaussian Skills: 3D Spatial Intelligence Open-Source Toolbox for 3D Gaussian Splatting Research},
  year = {2026},
  url = {https://github.com/jaccen/Awesome-Gaussian-Skills}
}

Acknowledgments

• 3D Gaussian Splatting —The foundational work
• awesome-3D-gaussian-splatting —The awesome list that inspired this project
• Awesome3DGS/3D-Gaussian-Splatting-Papers —Comprehensive paper collection
• All 3DGS researchers whose papers form our knowledge base

License

Apache-2.0. See LICENSE for details.

Sponsor & Community

If this project helps your research or work, consider supporting us!

Sponsor / 打赏 Buy us a coffee

Join Group / 加入社群 3DGS research discussion

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