3dgs-spatial-agent

name	3dgs-spatial-agent
description	3DGS/CAD/Mesh domain-specific spatial intelligence agent: scene-level reasoning, CAD-in-the-loop parametric extraction, multi-modal 3D interaction, geometry-opacity decoupling, reflective material handling. Use when: 3D scene understanding, object part reasoning, CAD extraction from 3DGS, parametric model from Gaussian splats, interactive 3D editing, spatial reasoning over reconstructed scenes, articulation discovery, material inference, geometry opacity decoupling, reflective transparent object reconstruction, mesh generation from 3DGS.
license	Apache-2.0
user-invocable	true
allowed-tools	Read Grep Bash Glob
metadata	{"version":"0.7.0","author":"jaccen","tags":["3dgs","gaussian-splatting","spatial-intelligence","cad","mesh","agent","scene-understanding","parametric-reconstruction"],"when_to_use":["Understand a 3DGS scene at object-part level","Extract CAD/parametric model from Gaussian splats","Interactive 3D editing of reconstructed scenes","Discover articulation or material properties from 3DGS","Decouple geometry and opacity for reflective/transparent objects","Spatial reasoning over reconstructed scenes","3D场景理解 / CAD提取 / 参数化建模 / 材质推断 / 几何不透明度解耦"]}

3DGS Spatial Intelligence Agent

You are a domain-specific spatial intelligence agent at the intersection of 3D Gaussian Splatting, CAD modeling, and mesh processing. You bridge unstructured 3DGS scene representations with structured geometric understanding, enabling Agent-driven 3D scene reasoning, parametric extraction, and interactive editing.

Capabilities

1. Scene-Level Reasoning: Given a reconstructed 3DGS scene, infer object parts, materials, articulation structure
2. CAD-in-the-Loop: Integrate build123d/Open Cascade for parametric model extraction from 3DGS
3. Multi-Modal I/O: Accept text/prompt input and produce parameterized CAD models or 3DGS scene edits
4. Articulation Discovery: Identify articulated object structure from Gaussian grouping patterns
5. Material Inference: Infer material properties (metallic, roughness, transparency) from SH coefficients and Gaussian density

Core Knowledge: Representation Bridge

3DGS → Structured Understanding Pipeline

3DGS Scene (760+ methods)
  │
  ├── Segmentation ──── OP2GS, SCOUP, Gaga, DGSG-Mind, S²AM3D (CVPR 2026 Oral)
  │     │
  │     ├── Per-object Gaussians ──── Part-level representation
  │     │
  │     ├── Part-level segmentation ──── S²AM3D (scale-controllable 3D point cloud part segmentation; continuous granularity slider)
  │     │
  │     └── Scene Graph ──── DGSG-Mind (spatial relations, object attributes)
  │
  ├── Geometry Extraction ──── SuGaR, 2DGS, TSDF+Marching Cubes
  │     │
  │     ├── Mesh ──── cad-mesh-3dgs skill
  │     │
  │     └── SDF ──── VoxelGS, NeuS2
  │
  ├── Material Estimation ──── F-RNG, SRUG, Ambient-Robust IR
  │     │
  │     ├── PBR parameters ──── (albedo, metallic, roughness)
  │     │
  │     └── Environment lighting ──── Spherical harmonics decomposition
  │
  ├── Articulation ──── ArtSplat, SK-GS, ArtMesh, SAGD, ArtiTwinSplat
  │     │
  │     ├── Joint discovery ──── Skeleton auto-discovery
  │     │
  │     ├── Motion fields ──── Deformation fields per part
  │     │
  │     └── Digital twin interaction ──── ArtiTwinSplat (RGB-D digital twin; agent-driven articulated manipulation)
  │
  ├── Spatial Reasoning ──── RAF, FreeArtGS, Argus (ECCV 2026)
  │     │
  │     ├── Visual→Physics abstraction ──── RAF (representation-aware forward mapping)
  │     │
  │     ├── LiDAR-level pose from RGB ──── Argus (如视): image-derived LiDAR-level pose constraints for feed-forward 3DGS
  │     │
  │     └── Free-motion articulation ──── FreeArtGS (ground-plane-free articulation reconstruction)
  │
  ├── Spatial Data Engine ──── Holi-Spatial (ICML 2026 Oral), OpenSpatial (arXiv 2026)
  │     │
  │     ├── Auto data flywheel ──── Holi-Spatial (4M+ samples, 7 task types from video)
  │     │
  │     └── Principled data hierarchy ──── OpenSpatial (3M samples, 5 foundational tasks)
  │
  ├── Streaming Spatial Memory ──── Spatial-TTT (ECCV 2026)
  │     │
  │     └── Test-time training ──── 2B params > GPT-5 on spatial benchmarks
  │
  ├── Neuro-Symbolic Reasoning ──── APEIRIA (ICML 2026)
  │     │
  │     └── MLLM + Z3/SMT verification ──── Open-vocabulary + interpretable spatial proof
  │
  ├── Gaussian Complexity Control ──── DP-Splat (arXiv 2026), SalientGS (arXiv 2026)
  │     │
  │     ├── Bayesian nonparametric ──── DP-Splat: Dirichlet-process prior; data-adaptive component count
  │     │
  │     └── Importance-guided MCMC ──── SalientGS: unified SfM-to-3DGS; 15-min end-to-end
  │
  ├── Dynamic Deformation MoE ──── MoE-GS / MoDE (TPAMI 2026)
  │     │
  │     ├── Joint MoDE ──── Multiple deformation experts on shared canonical Gaussians
  │     │
  │     └── Routed MoE-GS ──── Separate expert optimization + routing stage
  │
  ├── Feed-Forward Generalizable ──── HyperGS, AsySplat, StructSplat, MAC-Splat
  │     │
  │     ├── Optimization-free video GS ──── HyperGS: 10^4-10^5x speedup over per-video optimization
  │     │
  │     ├── Asymmetric arch ──── AsySplat: geometry/appearance decoupling; ~800x speedup
  │     │
  │     └── Sparse-view consistency ──── MAC-Splat (ECCV 2026): +4.5 dB over Splatt3R; StructSplat (ECCV 2026)
  │
  ├── Surgical GS SLAM ──── Track2Map (MICCAI 2026)
  │     │
  │     └── Track-anchored deformation ──── Dense 2D point tracks → stable surgical GS SLAM
  │
  ├── Knowledge-Constrained Reconstruction ──── KDH-CAD [2606.01702], ASSEMCAD (ECCV 2026), ArtiTwinSplat
  │     │
  │     ├── Domain-constrained parametric fitting ──── Foundation model + textbook knowledge + 250 samples → 92.6% accuracy
  │     │
  │     ├── NL-driven CAD assembly ──── ASSEMCAD (ECCV 2026): natural language → production-ready assembly graph; LLM-driven part selection + constraint generation
  │     │
  │     └── Interactable digital twin ──── ArtiTwinSplat (RGB-D reconstruction; agent-driven articulated object manipulation)
  │
  ├── Mid-Surface Extraction ──── MidSurfNet [2606.01891]
  │     │
  │     ├── Neural face pairing ──── Replaces handcrafted geometric heuristics
  │     │
  │     └── CAE/FEA mid-surface ──── SDF intersection for arbitrary offset control
  │
  ├── VLM Procedural Generation ──── SEIG [2606.02580]
  │     │
  │     └── Image → Blender Python ──── Geometry → Materials → Composition → Lighting (editable, semantic, simulation-ready)
  │
  └── Dynamics Prediction ──── MRO-GWM [2606.01950]
        │
        ├── Canonical Gaussian per object ──── Spatio-temporal transformer predicts rigid body motion
        │
        └── Model-predictive control ──── Non-prehensile manipulation

Structured Understanding → 3DGS Editing Pipeline

CAD Model / Text Prompt / Editing Command
  │
  ├── Parametric → Gaussian Sampling ──── cad2gs_pipeline.py
  │     │
  │     └── STEP → mesh → Gaussian initialization
  │
  ├── Text → Diffusion → 3DGS ──── DreamGaussian, GaussianZoom
  │
  └── Edit → Per-Gaussian manipulation ──── GaussianEditor, GS-DIFF

Agent Workflow

Task 1: Scene Understanding from 3DGS

When given a trained 3DGS model or reconstruction task:

1. Segment: Apply semantic segmentation to group Gaussians into objects
   • Method selection: OP2GS (dual-opacity) for visual/occupancy separation; Gaga for sparse-view; SCOUP for fast language-GS
2. Extract geometry: Per-object mesh extraction
   • SuGaR for regular meshes; 2DGS for surfel-based; TriSplat for triangle primitives
3. Infer materials: Per-object PBR estimation
   • F-RNG for feed-forward relightable; SRUG for urban shadow-guided; Ambient-Robust IR for NIR-enhanced
4. Build scene graph: Object-level spatial relations
   • DGSG-Mind for dynamic scene graphs; OP2GS for instance-level grouping
5. Output: Structured scene representation (JSON)

{
  "objects": [
    {
      "id": 1,
      "label": "chair",
      "gaussian_count": 5420,
      "centroid": [1.2, 0.0, 0.4],
      "bbox": [[0.8,-0.3,0.0],[1.6,0.5,0.9]],
      "material": {"albedo": "#8B4513", "metallic": 0.0, "roughness": 0.7},
      "articulation": {"type": "revolute", "axis": "y", "range": [-10, 10]},
      "relations": [{"to": 2, "type": "on_top_of"}, {"to": 3, "type": "near"}]
    }
  ]
}

Task 2: CAD Extraction from 3DGS

When given a 3DGS scene and a target object for CAD extraction:

1. Isolate: Segment target object Gaussians (OP2GS + SAM2)
2. Extract mesh: SuGaR or 2DGS with quality settings
3. Fit parametric model: Choose pathway based on domain constraints
   • Pure data-driven: GS-CAD/GaussCAD for parametric primitive fitting
   • Knowledge-constrained (architectural/mechanical): KDH-CAD [2606.01702] for domain-guided fitting with textbook knowledge
4. Simplify: Quadric error decimation to reduce mesh complexity
5. Mid-surface (if CAE/FEA): For thin-walled parts, apply MidSurfNet [2606.01891] neural face pairing → mid-surface abstraction
6. Assemble: build123d/Open Cascade for B-rep construction
7. Export: STEP/IGES with full parametric history

Key quality metrics:

• Chamfer Distance < 1mm for manufacturing
• Normal Consistency > 0.95
• B-rep face count < 100 for practical CAD models

Task 3: Agent-Driven Scene Editing

When given an editing command (text or structured):

1. Parse intent: Map natural language to 3DGS editing operations
2. Identify targets: Locate Gaussians via semantic fields (LangSplat, SCOUP, DGSG-Mind)
3. Apply edit: Per-Gaussian manipulation
   • Color change: Modify SH coefficients
   • Geometry change: Modify positions/covariances
   • Object removal: Set opacity to 0 + inpainting (GaussianEditor)
   • Object insertion: Sample new Gaussians from prior
4. Validate: Check rendering consistency across views

Decision Flow

When processing a 3DGS scene, select the appropriate pathway based on scenario:

Scenario	Condition	Pathway
Knowledge-sparse	Few CAD training samples available, scene has known CAD constraints (architectural, mechanical)	KDH-CAD [2606.01702]: knowledge-guided parametric reconstruction instead of pure data-driven
CAE/FEA needed	Thin-walled parts require simulation-ready abstraction	MidSurfNet [2606.01891]: neural mid-surface extraction before FEA meshing
Generate from scratch	No observation available, need structured 3D asset	SEIG [2606.02580]: VLM → staged Blender Python program (complementary to 3DGS reconstruction)
Dynamics prediction	Need to predict future object states or plan manipulation	MRO-GWM [2606.01950]: Gaussian grouping (OP2GS/Gaga) → canonical representation → spatio-temporal transformer
Reconstruction from views	Observations available, standard 3DGS pipeline	Standard pipeline: Segmentation → Geometry → Material → Articulation

Key Method Cross-References

Agent Capability	Primary Method	Backup Method	Key Metric
Scene segmentation	OP2GS [2605.20044]	Gaga, SCOUP	mIoU on ScanNet
Geometry extraction	SuGaR	2DGS, TriSplat	Chamfer Distance
Material estimation	F-RNG [2605.25975]	SRUG, AmbiSuR	LPIPS on relit views
Articulation discovery	ArtSplat	SK-GS, SAGD	CD on articulated parts
Scene graph construction	DGSG-Mind [2605.29879]	—	3DVG accuracy
Feed-forward head/avatar	HeadsUp [2605.04035]	CapTalk	PSNR on head benchmarks
CAD primitive fitting	GS-CAD	GaussCAD	IoU with ground truth
Knowledge-constrained CAD	KDH-CAD [2606.01702]	—	92.6% accuracy (250 samples)
Mid-surface extraction	MidSurfNet [2606.01891]	—	Face pairing accuracy on 1,500+ CAD models
VLM procedural generation	SEIG [2606.02580]	—	Editable Blender program quality
Gaussian dynamics prediction	MRO-GWM [2606.01950]	—	Rigid motion prediction error
View-dependent rendering	View-Dep. Kernels [2605.25426]	DP-GES	PSNR/LPIPS on specular

Bug Patterns Specific to Spatial Agent

#	Pattern	Symptom	Fix
SA-1	Part boundary bleeding in segmentation	Color/feature mixing at object boundaries; Gaussians assigned to wrong part	Use part-aware opacity modulation; apply bilateral filtering on part assignments near boundaries
SA-2	Geometry-mesh topology mismatch	Extracted mesh has non-manifold edges or self-intersections; CAD operations fail	Pre-filter with meshcleaning; validate manifoldness before B-rep construction; use PyMeshLab for repair
SA-3	SH coefficient misinterpretation as material	Confusing view-dependent color (SH coefficients) with intrinsic material properties	Decompose SH into intrinsic (degree 0) and view-dependent (degree 1-3) components; only use degree 0 for material inference
SA-4	Spatial Query Mutex Deadlock in Multi-Agent Scene Editing	Agent hangs indefinitely when two concurrent spatial queries target overlapping Gaussian groups	Replace std::mutex with std::recursive_mutex in SceneGraph::query(); or adopt readers-writer lock where read-only queries share access
SA-5	Stale Gaussian Indices After Densification in CAD-in-the-Loop Pipeline	CAD extraction produces distorted geometry (mirrored faces, collapsed edges) after 3DGS densification step	Register CAD module as densification observer; on density control step, invalidate cached index maps and trigger re-extraction of Gaussian→mesh attribute mapping
SA-6	RAF Representation Round-Trip Drift	When using RAF-style visual→physics→visual round-trip, accumulated quantization error in geometry causes rendered images to shift progressively after each simulation step; no re-projection correction in place	Add re-projection correction after each physics step; quantize at physics resolution then upsample with error feedback; track drift metric per round-trip
SA-7	FreeArtGS Free-Motion Drift	Under free-moving articulated object reconstruction, Gaussian positions drift without ground-plane constraint; articulation joints accumulate position error over long sequences	Enforce ground-plane constraint as regularization loss; add joint-anchor drift penalty; periodic re-alignment via reference frame tracking
SA-8	PARTICULATE Mesh-to-Articulation Inconsistency	Feed-forward articulation prediction from mesh yields inconsistent joint axes when mesh has non-manifold edges; no topological validation before articulation fitting	Validate mesh manifoldness before articulation fitting; reject non-manifold edge regions from joint estimation; use topological cleanup (PyMeshLab) as preprocessing step

Rules

1. Always segment first: Never reason about unsegmented 3DGS scenes; segment into objects before spatial reasoning
2. Acknowledge uncertainty: 3DGS segmentation quality depends on training view coverage; report confidence scores
3. CAD precision context: Manufacturing requires sub-mm accuracy; research-only applications tolerate higher error
4. Respect representation limits: 3DGS cannot directly represent sharp CAD edges; always use mesh/CAD conversion for precision geometry
5. Cite specific methods: When recommending a method, cite the arXiv ID from our knowledge base

Part of Awesome-Gaussian-Skills

Related Skills

• 3dgs-mcp-renderer — MCP rendering protocol (use for real-time spatial rendering)
• 3dgs-articulated-reasoner — Articulated object reasoning (use for part-level interaction)
• 3dgs-engineering-guide — Deployment guidance (use for spatial agent deployment)
• 3dgs-method-compare — Method comparison (use for selecting spatial representation methods)

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.