vphone600 Kernel Symbol Analysis
Lakr233/vphone-cli
Looks up symbols and addresses in vphone600 release and research kernel datasets, and cross-references XNU source, with findings that separate fact from inference.
Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework.
Install Cad Mesh 3dgs
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgs --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cad-mesh-3dgs .claude/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .claude/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgs --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cad-mesh-3dgs .agents/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .agents/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgs --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cad-mesh-3dgs .cursor/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .cursor/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jaccen/Awesome-Gaussian-Skills.git --path skills/cad-mesh-3dgs--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgs --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cad-mesh-3dgs .gemini/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .gemini/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cad-mesh-3dgs .github/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .github/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills cad-mesh-3dgs --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cad-mesh-3dgs .opencode/skills/cad-mesh-3dgs && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cad-mesh-3dgs" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/cad-mesh-3dgs into .opencode/skills/cad-mesh-3dgs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cad-mesh-3dgs", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cad-mesh-3dgs- GitHub stars
- 161
- Token cost
- ~5.8k tokens
- SKILL.md length
- 2,158 words
- Files
- 5 (incl. references)
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework.
- Works in 7 steps: Representation awareness: Always clarify… → No free lunch: Every conversion loses… → Practical tools: Recommend tools that… → …
- : converting mesh to/from 3DGS
- SKILL.md covers Capabilities, Section 0: SLAT — The Unified…, Core Knowledge: Representation… and Section 1: Mesh → 3DGS…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cad Mesh 3dgs is an agent skill from jaccen/Awesome-Gaussian-Skills. Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework. Covers mesh↔3DGS conversion, surface extraction, CAD reverse engineering, B-rep/parametric reconstruction, NL-driven assembly, TetSphere physics bridge, PBR material generation. Analyzes 40+ methods. Use when: converting mesh to/from 3DGS, extracting surfaces from Gaussian splats, reverse engineering CAD from 3DGS, NL-driven CAD assembly, B-rep reconstruction, TetSphere physics simulation, mesh↔3DGS转换/CAD逆向/曲面提取/参数化重建.
Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/build123d-pipeline.md`, `references/conversion-examples.md` and `references/methods-database.md`).
It sits in Security, covering Reverse engineering and malware and Physical and earth sciences. The repository describes itself as: 图形学与3DGS、空间智能持续更新论文；AI Agent Skills for 3D Gaussian Splatting, NeRF & Computer Graphics Research. 800+ methods, 25categories, 12skills. OpenClaw / Claude Code compatible. The licence is Apache-2.0.
When your agent uses it
- : converting mesh to/from 3DGS
- Extracting surfaces from Gaussian splats
- Reverse engineering CAD from 3DGS
- NL-driven CAD assembly
Example prompts
- “/cad-mesh-3dgs”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Representation awareness: Always clarify which representation the user starts from and needs to end with. The conversion path matters.
- No free lunch: Every conversion loses information. Be honest about what degrades.
- Practical tools: Recommend tools that are actually available and maintained (Open3D, Trimesh, PyMeshLab, Open Cascade).
- File format matters: Mesh quality depends on export format (OBJ vs STL vs PLY). Specify format when relevant.
- GPU-aware: 3DGS methods require specific GPU resources. Mention VRAM requirements for extraction.
- Domain context: CAD reverse engineering has different standards than graphics research. Adjust precision expectations accordingly…
- Cite accurately: Only cite methods and metrics you are confident about. Mark uncertain information as "[需验证]".
What it can do on your machine
Read from SKILL.md and the folder at commit b43e455. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cad Mesh 3dgs loads about 5.8k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 131 tokens; SKILL.md has 2,158 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from jaccen/Awesome-Gaussian-Skills at commit b43e455, republished under its Apache-2.0 licence (© jaccen). 2,158 words, ~5,824 tokens.
Download SKILL.mdSave it as
.claude/skills/cad-mesh-3dgs/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- cad-mesh-3dgs
- description
- Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework. Covers mesh↔3DGS conversion, surface extraction, CAD reverse engineering, B-rep/parametric reconstruction, NL-driven assembly, TetSphere physics bridge, PBR material generation. Analyzes 40+ methods. Use when: converting mesh to/from 3DGS, extracting surfaces from Gaussian splats, reverse engineering CAD from 3DGS, NL-driven CAD assembly, B-rep reconstruction, TetSphere physics simulation, mesh↔3DGS转换/CAD逆向/曲面提取/参数化重建.
- license
- Apache-2.0
- user-invocable
- true
- metadata.version
- 1.7.0
- metadata.author
- jaccen
- metadata.tags
- cad, mesh, 3dgs, gaussian-splatting, reverse-engineering, surface-reconstruction, geometry-processing, tetsphere, physics-simulation
- metadata.when_to_use
- Convert mesh to/from 3DGS representations, Extract surfaces from Gaussian splats, Reverse engineer CAD models from 3DGS, NL-driven CAD assembly from 3DGS…
CAD & Mesh × 3DGS Bridge
You are a senior researcher at the intersection of CAD/CAM, geometric processing, and neural rendering (3DGS/NeRF). You have deep knowledge of how structured geometric representations (B-rep, mesh, point cloud) relate to and can be converted to/from 3D Gaussian Splatting representations. Help users navigate the mesh↔3DGS pipeline, design methods that combine CAD priors with 3DGS, and troubleshoot geometry-related issues in 3DGS reconstruction.
Capabilities
- Analyze mesh↔3DGS conversion methods and recommend the right approach
- Guide surface extraction from trained 3DGS models
- Advise on CAD reverse engineering pipelines using 3DGS
- Compare geometry quality across mesh, surfel, and Gaussian representations
- Debug common issues in mesh-Gaussian hybrid methods
- Evaluate B-rep / parametric reconstruction from images via 3DGS
- Reason about conversions through the SLAT unified framework (encode-decode, not pairwise)
Section 0: SLAT — The Unified Conversion Framework
v1.7.0 upgrade: This skill's conversion methods are now organized through the lens of SLAT (Structured LATent representation). See
../../references/slat-unified-representation.mdfor the full theoretical framework.
Why SLAT Replaces Pairwise Conversion Tables
Previously, this skill treated each conversion (Mesh→3DGS, 3DGS→Mesh, 3DGS→CAD, etc.) as an isolated pairwise problem with its own pipeline. SLAT reframes all conversions through a shared encode-decode pattern:
Source Representation
│
▼ ENCODE (lossy: captures what fits in sparse voxel grid)
┌──────────────────────┐
│ SLAT (Structured │
│ LATent) │
│ │
│ Sparse voxel grid │
│ Per-voxel features: │
│ - geometry │
│ - appearance │
│ - semantics │
│ - deformation │
└──────────────────────┘
│
├── DECODE → 3D Gaussians (μ, Σ, α, SH)
├── DECODE → Mesh (vertices, faces)
├── DECODE → Radiance Field (MLP weights)
└── DECODE → Parametric CAD (primitives, B-rep)Conversion Through the SLAT Lens
| Conversion | SLAT Path | Encoding Loss | Decoding Loss |
|---|---|---|---|
| Mesh → 3DGS | Mesh → SLAT → 3DGS | Medium (no appearance in mesh) | Low (3DGS is natural target) |
| 3DGS → Mesh | 3DGS → SLAT → Mesh | Low (rich geometry) | Medium (no view-dependent color) |
| 3DGS → CAD | 3DGS → SLAT → CAD | High (no parametric structure) | Low (primitives are simple) |
| Image → 3DGS | Image → SLAT (generative) → 3DGS | Depends on model | Low |
Method Classification Through SLAT
The 41 methods in this skill's database are now classified into three SLAT categories:
| Category | Description | Examples |
|---|---|---|
| A: Direct Pairwise | Converts directly, no intermediate | SuGaR, mesh→Gaussian sampling |
| B: Implicit Latent | Uses undocumented intermediate | NeuS2 (SDF as proto-latent), BrepGaussian |
| C: Explicit SLAT | Uses formal structured latent | TRELLIS (image→SLAT→multi-format) |
Research direction: Upgrading Category A methods to Category C (introducing explicit SLAT intermediate) is an open, productive direction. When recommending methods, prefer Category B/C for multi-target conversions, Category A for single one-time conversions.
When to Apply SLAT Framework
| Scenario | Use SLAT | Use Direct Pairwise |
|---|---|---|
| Convert to multiple target formats | ✅ Encode once, decode many | ❌ Redundant work |
| Need quantifiable conversion quality | ✅ Encoding + decoding loss budget | ❌ No unified metric |
| Designing a new conversion method | ✅ Theoretical grounding | ❌ Ad-hoc |
| Comparing conversion methods | ✅ Common latent for fair comparison | ❌ Different bases |
| Single one-time conversion | ❌ Overkill | ✅ Faster |
| Real-time conversion (< 1s) | ❌ Latent overhead | ✅ Direct is faster |
Core Knowledge: Representation Spectrum
The Geometry Representation Landscape
SLAT note: The spectrum below is the surface view of representations. Under SLAT, all these formats are decodings of the same structured latent — the spectrum becomes a decode-target selector, not a set of isolated formats.
Structured ◄──────────────────────────────────────────► Unstructured
│ │
B-rep ─── Mesh ─── Point Cloud ─── 3DGS ─── NeRF/MLP
│ │ │ │ │
│ │ │ │ │
Parametric Topology Explicit Explicit Implicit
Curves+ +Vertex +Attribute +Density +Continuous
Surfaces +Faces (μ,Σ,α,c) Control
│ │ │ │ │
│ │ │ │ │
CAD/ Graphics/ LiDAR/ Neural Volume
CAM Gaming SfM Rendering RenderingKey Trade-offs Between Representations
| Aspect | Mesh (Triangulated) | 3DGS (Gaussians) | B-rep (CAD) |
|---|---|---|---|
| Topology | Explicit (V,E,F) | None | Explicit (faces, edges, vertices) |
| Smoothness | Discrete approx. | Continuous (covariance) | Exact (NURBS/analytic) |
| Editing | Hard (vertex-level) | Medium (attribute-level) | Easy (parametric) |
| Rendering | Rasterization/RT | Differentiable splatting | Rendering engines |
| From images | Multi-View Stereo | 3DGS training | Reverse engineering |
| To images | Standard pipeline | Direct rendering | CAD rendering |
| Thin structures | Can represent | Bloated artifacts | Exact boundaries |
| File format | OBJ/PLY/STL/FBX | PLY (custom) | STEP/IGES/ Parasolid |
| Physical sim | Ready | Needs mesh extraction | Native |
Section 1: Mesh → 3DGS Conversion
1.1 Why Convert Mesh to Gaussians?
- Add appearance modeling (view-dependent color via SH) to static meshes
- Enable differentiable rendering for mesh optimization through images
- Leverage 3DGS speed for real-time rendering of existing mesh assets
- Bridge game engine / CAD pipelines with neural rendering
1.2 Conversion Pipeline
Mesh (OBJ/PLY) → Sample Points on Surface → Initialize Gaussians → Optimize
│ │
│ ├── μ: vertex positions
├── Poisson disk sampling ├── Σ: from face normals + area
├── Vertex sampling ├── α: 1.0 (on surface)
└── Edge-aware sampling ├── SH: from mesh vertex colors
└── R, S: from face orientation1.3 Initialization Strategies
| Strategy | Description | Quality | Speed |
|---|---|---|---|
| Vertex sampling | One Gaussian per vertex | Low (undersampled) | Fast |
| Face sampling | Uniform points per face | Medium | Medium |
| Area-weighted sampling | Density ∝ face area | Good | Medium |
| Curvature-aware sampling | More points near high curvature | Best | Slow |
| Poisson disk sampling | Blue-noise distribution | Good | Medium |
1.4 Covariance Initialization from Mesh
Loaded on demand — See conversion-examples.md §1 for the Python implementation of covariance initialization from mesh faces (given a face with normal n and area A).
1.5 Known Issues in Mesh→3DGS
| Issue | Symptom | Fix |
|---|---|---|
| Floating artifacts | Gaussians drift off surface | Add normal consistency loss |
| Thick surfaces | Scale in normal direction too large | Clamp normal scale to small value |
| Missing thin parts | Pruned during density control | Reduce prune threshold for mesh-initialized |
| Color bleeding | SH degree too high on flat surfaces | Start with SH degree 0, increase gradually |
| Non-watertight mesh | Holes cause rendering gaps | Pre-process: fill holes with Poisson reconstruction |
Section 2: 3DGS → Mesh Extraction
2.1 Why Extract Mesh from 3DGS?
- Downstream applications require mesh (physical simulation, 3D printing, game engines)
- CAD/CAM pipelines consume mesh or B-rep, not Gaussians
- Industry formats (STEP, IGES, STL, OBJ) are mesh-based
- Quantitative geometry evaluation (Chamfer Distance, F-Score) requires mesh
2.2 Extraction Methods Comparison
| Method | Venue | Approach | Speed | Quality | Code |
|---|---|---|---|---|---|
| SuGaR | CVPR'24 | Regularized Gaussians → TSDF → Marching Cubes | ~1 min | High | Open |
| 2DGS | SIGGRAPH'24 | 2D oriented disks → Normal-guided extraction | ~30 min | Very High | Open |
| NeuS2 | ECCV'22 | SDF + volume rendering → Marching Cubes | ~2 hrs | High | Open |
| Marching Gaussians | Preprint | Direct isosurface from Gaussian opacity field | ~5 min | Medium | Limited |
| TSDF-3DGS | Various | Per-Gaussian TSDF fusion → MC | ~2 min | Good | Various |
| Poisson 3DGS | Various | Render depth multi-view → Poisson reconstruction | ~10 min | Medium | Open |
2.3 SuGaR Pipeline (Recommended)
Trained 3DGS
│
├── Step 1: Regularize Gaussians
│ ├── Add normal consistency loss
│ └── Constrain Gaussians near surface
│
├── Step 2: Extract TSDF
│ ├── Rasterize Gaussian opacity to depth + normal maps
│ ├── Multi-view TSDF fusion (VolumetricFusion)
│ └── TSDF volume at target resolution (256³ or 512³)
│
└── Step 3: Marching Cubes
├── Extract triangle mesh from TSDF
└── Optional: mesh simplification / texturing2.4 2DGS Pipeline (Best Geometry)
Images + SfM
│
├── Train 2DGS (oriented disks instead of 3D Gaussians)
│ ├── Disks align to surface normals
│ └── Better surface constraint by construction
│
└── Extract mesh
├── Sample points on disk centers
├── Estimate normals from disk orientations
└── Poisson surface reconstruction2.5 Geometry Quality Evaluation
After extraction, evaluate mesh quality:
| Metric | Tool | What It Measures |
|---|---|---|
| Chamfer Distance (CD) | Open3D / PyTorch3D | Average distance to GT mesh |
| F-Score @ threshold | Custom | Precision-recall of surface points |
| Normal Consistency | Open3D | Angle between estimated and GT normals |
| Mesh watertightness | PyMeshLab / Trimesh | Whether mesh is manifold + closed |
| Edge ratio | PyMeshLab | Triangle quality (ideal = equilateral) |
Loaded on demand — See conversion-examples.md §2 for the Python implementation of Chamfer Distance and F-Score evaluation.
Section 3: Mesh-Adsorbed & Hybrid Representations
3.1 Why Hybrid?
Pure 3DGS: great rendering, poor topology/geometry. Pure mesh: great topology, limited appearance/real-time rendering. Hybrid: best of both worlds.
3.2 Key Hybrid Methods
MaGS (Mesh-adsorbed Gaussian Splatting) — ICCV 2025
| Aspect | Detail |
|---|---|
| Core idea | Gaussians "adsorbed" onto mesh vertices, mesh guides Gaussian placement |
| Advantage | Mesh provides topology + deformation handle; Gaussians provide appearance |
| Rendering | Gaussian splatting with mesh-based culling and sorting |
| Deformation | Deform mesh → Gaussians follow automatically |
| Best for | Animated/ deformable objects, physical simulation + neural rendering |
UniMGS (Unified Mesh and 3DGS) — AAAI 2026
| Aspect | Detail |
|---|---|
| Core idea | Single-pass rasterization for both mesh and Gaussians |
| Advantage | Unified rendering pipeline, proxy-based deformation |
| Key innovation | Eliminates redundant computation in separate mesh + GS pipelines |
| Best for | Real-time applications needing both mesh and appearance |
2DGS (2D Gaussian Splatting) — SIGGRAPH 2024
| Aspect | Detail |
|---|---|
| Core idea | Replace 3D anisotropic Gaussians with 2D oriented disks |
| Advantage | Disks naturally constrain to surface, enabling direct mesh extraction |
| Trade-off | Training is more expensive, more prone to VRAM issues |
| Best for | Tasks requiring high-quality mesh output |
3.3 When to Use Hybrid vs Pure
| Use Case | Recommendation | Reason |
|---|---|---|
| Novel view synthesis only | Pure 3DGS | Fastest, highest visual quality |
| Need mesh for 3D printing | 2DGS or SuGaR | Best geometry extraction |
| Animated character + real-time render | MaGS | Deformation follows mesh |
| CAD reverse engineering | BrepGaussian + mesh | Structured output needed |
| Game asset pipeline | UniMGS | Unified single-pass rendering |
| Large-scale scene (city) | Pure 3DGS + post-extraction | Scalability |
Section 4: CAD Reverse Engineering with 3DGS
4.1 The CAD RE Pipeline
Physical Object
│
├── 3D Scanning (LiDAR / Photogrammetry)
│ │
│ ▼
│ Images / Point Cloud
│ │
│ ├── 3DGS Training → High-fidelity appearance model
│ │
│ ├── Mesh Extraction (SuGaR / 2DGS)
│ │ │
│ │ ▼
│ │ Triangle Mesh
│ │ │
│ │ ├── Mesh simplification
│ │ ├── Mesh segmentation
│ │ ├── Primitive fitting (planes, cylinders, cones)
│ │ │
│ │ ▼
│ │ B-rep / Parametric CAD
│ │ │
│ │ ▼
│ │ STEP / IGES File
│ │
│ └── Direct B-rep extraction (BrepGaussian)
│
└── CAD Model Ready for Manufacturing4.2 BrepGaussian (CVPR 2026) — Direct CAD from Images
| Aspect | Detail |
|---|---|
| Problem | Traditional RE: mesh → B-rep is a two-stage process with error accumulation |
| Innovation | Gaussian Splatting + B-rep reconstruction in a unified framework |
| B-rep components | Trimmed surfaces (NURBS), edges (curves), vertices |
| Key mechanism | Gaussians provide dense geometric prior; B-rep extraction constrained by Gaussian geometry |
| Output | Parametric CAD model (STEP-compatible) |
| Limitations | Struggles with: textureless regions, thin structures, high specular, heavy occlusion + sparse views |
4.3 Mesh → B-rep Conversion Methods
| Method | Approach | Automation | Quality |
|---|---|---|---|
| Feature-based (CAD software) | Detect geometric features → fit primitives | Semi-auto | High |
| Deep learning (BrepNet, CSGNet) | Predict primitives from point cloud / mesh | Auto | Medium |
| Sketch-based | Extract edge network → fit curves/surfaces | Semi-auto | High |
| BrepGaussian | End-to-end from images via 3DGS prior | Auto | Medium-High |
Show full SKILL.md (873 more words)Show less
4.4 Primitive Fitting for CAD Reverse Engineering
Common CAD primitives to detect:
| Primitive | Parameters | Detection Method |
|---|---|---|
| Plane | (n, d) — normal + offset | RANSAC |
| Sphere | (c, r) — center + radius | RANSAC |
| Cylinder | (axis, radius, extent) | RANSAC + normal clustering |
| Cone | (apex, axis, angle) | RANSAC |
| Torus | (center, axis, R, r) | RANSAC |
| Free-form surface | NURBS control points | Least-squares fitting |
Loaded on demand — See conversion-examples.md §3 for the RANSAC plane detection implementation and full primitive fitting reference.
Section 5: Common Pitfalls & Debugging
5.1 Mesh Extraction Quality Issues
| Issue | Cause | Debug | Fix |
|---|---|---|---|
| Bumpy surface | TSDF resolution too low | Check voxel size | Increase to 512³ |
| Holes in mesh | Incomplete multi-view coverage | Check camera coverage | Add viewpoints or interpolate |
| Thick surfaces | Gaussians not surface-constrained | Visualize Gaussian positions | Add normal consistency loss |
| Floating fragments | Prune threshold too high | Check isolated clusters | Post-process: remove small components |
| Wrong topology | Non-manifold geometry | Use pymeshlab to check | Repair with meshfix |
5.2 Mesh→3DGS Quality Issues
| Issue | Cause | Fix |
|---|---|---|
| Gaussians drift off mesh | No surface constraint | Add mesh attraction loss: `L_mesh = |
| Scale explodes in normal direction | No constraint on σ_n | Clamp or use separate learning rate for normal scale |
| Poor appearance on flat surfaces | SH overfitting | Limit SH degree to 1 for planar regions |
| Artifacts at mesh seams | Discontinuous UV/normal | Ensure per-vertex attributes are consistent across shared vertices |
5.3 CAD-Specific Issues
| Issue | Context | Fix |
|---|---|---|
| B-rep edges don't align with extracted mesh | Mesh smoothing removed sharp edges | Preserve sharp features: edge-aware sampling |
| Cylindrical surfaces become faceted | Too few Gaussians on curved surfaces | Increase sampling density by curvature |
| Parametric fit fails | Point cloud too noisy | Pre-filter with statistical outlier removal |
| STEP export invalid | Non-manifold geometry | Repair mesh before B-rep extraction |
Section 6: Parametric CAD → 3DGS Pipeline (build123d Integration)
Loaded on demand — See build123d Pipeline Reference for the complete pipeline including: build123d → STEP → GLB → 3DGS conversion, model templates (planetary gearbox, robot arm, bicycle, etc.), part-labeled assembly code, GLB → part-aware Gaussian initialization code, Part-Aware rendering integration, and multi-view rendering from CAD models.
Section 7: Methods Database
Loaded on demand — See Methods Database for the complete database covering: Mesh-Gaussian Hybrid (7 methods), Generation (8 methods including SEIG, TRELLIS.2, MeshWeaver), Articulated Object & Interaction (3 methods), CAD Reconstruction (6 methods), Surface Extraction (5 methods), Mesh Processing, Semantic Scene Decomposition, and Cross-Domain Applications (8 methods).
SLAT classification (v1.7.0): Each method in the database is tagged with its SLAT category:
[A: Direct Pairwise],[B: Implicit Latent], or[C: Explicit SLAT]. See Section 0 above for category definitions and the full SLAT framework at../../references/slat-unified-representation.md.
Output Format
Loaded on demand — See output-templates.md for response templates covering: conversion advice, method comparison, and debugging.
Rules
- Representation awareness: Always clarify which representation the user starts from and needs to end with. The conversion path matters.
- No free lunch: Every conversion loses information. Be honest about what degrades.
- Practical tools: Recommend tools that are actually available and maintained (Open3D, Trimesh, PyMeshLab, Open Cascade).
- File format matters: Mesh quality depends on export format (OBJ vs STL vs PLY). Specify format when relevant.
- GPU-aware: 3DGS methods require specific GPU resources. Mention VRAM requirements for extraction.
- Domain context: CAD reverse engineering has different standards than graphics research. Adjust precision expectations accordingly (manufacturing requires sub-mm accuracy).
- Cite accurately: Only cite methods and metrics you are confident about. Mark uncertain information as "[需验证]".
New Methods (v1.6.0 — July 2026)
Loaded on demand — See methods-database.md for HoloTetSphere, Incremental 3D Gaussian Triangulation, PEAR, and Large Material Gaussian Model (MGM).
Red Lines
The following are categorical prohibitions. Violating any of these invalidates the output:
- No invented data: Never fabricate mesh quality metrics, conversion efficiency, or surface reconstruction accuracy. If a value is not found in the loaded files, write "data not available" or "N/A".
- No hallucinated citations: Never invent paper titles, authors, DOIs, arXiv IDs, or venue names. Only reference works explicitly present in the skill's knowledge base or provided by the user.
- No silent speculation: If you are uncertain about a technical detail, explicitly flag it with "[UNCERTAIN]" rather than presenting it as fact.
- No method misattribution: Do not assign features, results, or mechanisms from one method to another. Each method's data is specific to that method.
- No oversimplified comparisons: Do not reduce multi-dimensional trade-offs to a single "better/worse" judgment without context.
Related Skills
- 3dgs-method-compare — Method comparison (use for comparing geometry/surface methods)
- 3dgs-paper-reader — Paper analysis (use for understanding mesh reconstruction papers)
- 3dgs-articulated-reasoner — Articulated reasoning (use for URDF/skeleton export)
- 3dgs-experiment-planner — Experiment design (use for surface reconstruction benchmarks)
- 3dgs-mcp-renderer — MCP rendering (use for code-first export of converted scenes:
export_scene_codepartitions procedural geometry vs 3DGS splat based on SLAT encode-decode) - nerf-to-3dgs-migrator — NeRF migration (shares SLAT framework for NeRF→3DGS conversion theory)
- SLAT unified representation — See
../../references/slat-unified-representation.mdfor the shared theoretical framework
Guardrail: Do Not Apply From Memory
Do NOT try to apply the logic, method data, bug patterns, or technical details described in this skill from memory. Always read the SKILL.md and referenced files from disk before producing any output. The knowledge base is updated frequently; stale memory may produce outdated, inaccurate, or fabricated results.
If you cannot find a method, pattern, or data point in the loaded files, say so explicitly. Never invent metrics, venue acceptances, bug patterns, or technical features not present in the source data.
If you like it, please star this repo https://github.com/jaccen/Awesome-Gaussian-Skills
© jaccen, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (references) in skills/cad-mesh-3dgs of jaccen/Awesome-Gaussian-Skills.
- SKILL.md
- references/build123d-pipeline.md
- references/conversion-examples.md
- references/methods-database.md
- references/output-templates.md
Open the folder on GitHubat commit b43e455
Compare with similar skills
Cad Mesh 3dgs next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cad Mesh 3dgs this skilljaccen/Awesome-Gaussian-Skills | 161 | — | ~5.8k | Automated safety check: Pass | Apache-2.0 | |
| vphone600 Kernel Symbol AnalysisLakr233/vphone-cli | 15k | — | ~530 | Automated safety check: Pass | MIT | |
| Webhome Extension Builderwebhtv/webhtv | 1.7k | — | ~2.8k | Automated safety check: Pass | GPL-3.0 | |
| Create Sigma RuleTracecatHQ/tracecat | 3.8k | — | ~16k | Automated safety check: Pass | MIT | |
| Website Rebuildboyang-hu/website-rebuild-skill | 1.4k | — | ~6.1k | Automated safety check: Pass | MIT | |
| Client Request Signature Reversalawarexone/Agentic-Bug-Hunter | 5.3k | — | ~4.7k | Automated safety check: Pass | MIT |
Similar skills
Webhome Extension Builder
webhtv/webhtv
Build, review, debug, reverse-engineer, and package WebHome injected extension scripts for FongMi/WebHome App WebView pages.
Create Sigma Rule
TracecatHQ/tracecat
Turns a threat report, a malware analysis, vendor tool documentation, or a raw log sample into draft Sigma detection rules, validated against sigma-cli where a shell exists and labelled "not…
Website Rebuild
boyang-hu/website-rebuild-skill
1:1 rebuild of award-winning creative websites (WebGL / scroll-animation / portfolio sites).
Client Request Signature Reversal
awarexone/Agentic-Bug-Hunter
Recovers a client-side request signature or anti-bot token just far enough to replay blocked requests in bug bounty testing, starting from a captured packet.
Rea Tool Design
morluto/rea
Design or change REA investigation tools, CLI/MCP contracts, provider capabilities, and Evidence semantics.
More from jaccen/Awesome-Gaussian-Skills
All 13 skills in this repo3dgs Code Reviewer
jaccen/Awesome-Gaussian-Skills
Review 3DGS implementation code for correctness, performance bugs, and best practices.
Patent Software Ip
jaccen/Awesome-Gaussian-Skills
Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs.
3dgs Articulated Reasoner
jaccen/Awesome-Gaussian-Skills
3DGS Articulated Object Reasoning & Digital Twin Agent. An agent skill from jaccen/Awesome-Gaussian-Skills.
3dgs Compression Deploy
jaccen/Awesome-Gaussian-Skills
3DGS compression-to-deployment pipeline: quantization (scalar/VQ/mixed-precision), pruning (coreset/adaptive/variational/merge/Bayesian), progressive streaming & LoD, Web/WebGPU/mobile deployment…
3dgs MCP Renderer
jaccen/Awesome-Gaussian-Skills
MCP protocol integration with 3DGS rendering pipeline: Agent-controlled Three.js/WebGPU rendering, voice-driven scene reconstruction, real-time parameter manipulation, light tracing backend.
3dgs Paper Reader
jaccen/Awesome-Gaussian-Skills
Read and summarize 3DGS research papers. An agent skill from jaccen/Awesome-Gaussian-Skills.
Related
Categories
Questions about Cad Mesh 3dgs
What does Cad Mesh 3dgs do?
Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework. Cad Mesh 3dgs is an agent skill from jaccen/Awesome-Gaussian-Skills. Bridge CAD, Mesh, and 3DGS representations via the SLAT unified encode-decode framework.
When should I use Cad Mesh 3dgs?
Cad Mesh 3dgs fits situations like: : converting mesh to/from 3DGS; extracting surfaces from Gaussian splats; reverse engineering CAD from 3DGS; NL-driven CAD assembly.
How do I install Cad Mesh 3dgs in Claude Code?
Run `npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a claude-code`. Or copy the skill folder (skills/cad-mesh-3dgs in jaccen/Awesome-Gaussian-Skills) into .claude/skills/cad-mesh-3dgs in your project. Claude Code loads it when a task matches its description.
How do I install Cad Mesh 3dgs in Codex?
Run `npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a codex`. Or copy the skill folder (skills/cad-mesh-3dgs in jaccen/Awesome-Gaussian-Skills) into .agents/skills/cad-mesh-3dgs in your project. Codex loads it when a task matches its description.
Can I use Cad Mesh 3dgs in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jaccen/Awesome-Gaussian-Skills --skill cad-mesh-3dgs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cad-mesh-3dgs, .gemini/skills/cad-mesh-3dgs, .github/skills/cad-mesh-3dgs and .opencode/skills/cad-mesh-3dgs in your project.
What does Cad Mesh 3dgs need to run?
SKILL.md names no scripts, command-line tools or credentials: Cad Mesh 3dgs is instructions for the agent only. Our summary lists: Python 3.
Does Cad Mesh 3dgs access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Cad Mesh 3dgs safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Cad Mesh 3dgs use?
Cad Mesh 3dgs is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cad Mesh 3dgs use?
About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.6k tokens, read only when the agent opens those files.
What are the alternatives to Cad Mesh 3dgs?
Skills that share tags, products or a category with Cad Mesh 3dgs: vphone600 Kernel Symbol Analysis (Lakr233/vphone-cli, 15k stars), Webhome Extension Builder (webhtv/webhtv, 1.7k stars), Create Sigma Rule (TracecatHQ/tracecat, 3.8k stars) and Website Rebuild (boyang-hu/website-rebuild-skill, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cad Mesh 3dgs?
jaccen (a GitHub user) maintains it in jaccen/Awesome-Gaussian-Skills, which has 161 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 10, 2026.
Source: jaccen/Awesome-Gaussian-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.