3dgs Spatial Agent

3DGS Scene (872 methods)
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  ├── Segmentation ──── OP2GS, SCOUP, Gaga, DGSG-Mind, S²AM3D (CVPR 2026 Oral)
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  │     ├── Per-object Gaussians ──── Part-level representation
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  │     ├── Part-level segmentation ──── S²AM3D (scale-controllable 3D point cloud part segmentation; continuous granularity slider)
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  │     └── Scene Graph ──── DGSG-Mind (spatial relations, object attributes)
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  ├── Geometry Extraction ──── SuGaR, 2DGS, TSDF+Marching Cubes
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  │     ├── Mesh ──── cad-mesh-3dgs skill
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  │     └── SDF ──── VoxelGS, NeuS2
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  ├── Material Estimation ──── F-RNG, SRUG, Ambient-Robust IR
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  │     ├── PBR parameters ──── (albedo, metallic, roughness)
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  │     └── Environment lighting ──── Spherical harmonics decomposition
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  ├── Articulation ──── ArtSplat, SK-GS, ArtMesh, SAGD, ArtiTwinSplat
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  │     ├── Joint discovery ──── Skeleton auto-discovery
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  │     ├── Motion fields ──── Deformation fields per part
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  │     └── Digital twin interaction ──── ArtiTwinSplat (RGB-D digital twin; agent-driven articulated manipulation)
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  ├── Spatial Reasoning ──── RAF, FreeArtGS, Argus (ECCV 2026)
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  │     ├── Visual→Physics abstraction ──── RAF (representation-aware forward mapping)
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  │     ├── LiDAR-level pose from RGB ──── Argus (如视): image-derived LiDAR-level pose constraints for feed-forward 3DGS
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  │     └── Free-motion articulation ──── FreeArtGS (ground-plane-free articulation reconstruction)
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  ├── Spatial Data Engine ──── Holi-Spatial (ICML 2026 Oral), OpenSpatial (arXiv 2026)
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  │     ├── Auto data flywheel ──── Holi-Spatial (4M+ samples, 7 task types from video)
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  │     └── Principled data hierarchy ──── OpenSpatial (3M samples, 5 foundational tasks)
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  ├── Streaming Spatial Memory ──── Spatial-TTT (ECCV 2026)
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  │     └── Test-time training ──── 2B params > GPT-5 on spatial benchmarks
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  ├── Neuro-Symbolic Reasoning ──── APEIRIA (ICML 2026)
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  │     └── MLLM + Z3/SMT verification ──── Open-vocabulary + interpretable spatial proof
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  ├── Gaussian Complexity Control ──── DP-Splat (arXiv 2026), SalientGS (arXiv 2026)
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  │     ├── Bayesian nonparametric ──── DP-Splat: Dirichlet-process prior; data-adaptive component count
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  │     └── Importance-guided MCMC ──── SalientGS: unified SfM-to-3DGS; 15-min end-to-end
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  ├── Dynamic Deformation MoE ──── MoE-GS / MoDE (TPAMI 2026)
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  │     ├── Joint MoDE ──── Multiple deformation experts on shared canonical Gaussians
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  │     └── Routed MoE-GS ──── Separate expert optimization + routing stage
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  ├── Feed-Forward Generalizable ──── HyperGS, AsySplat, StructSplat, MAC-Splat
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  │     ├── Optimization-free video GS ──── HyperGS: 10^4-10^5x speedup over per-video optimization
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  │     ├── Asymmetric arch ──── AsySplat: geometry/appearance decoupling; ~800x speedup
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  │     └── Sparse-view consistency ──── MAC-Splat (ECCV 2026): +4.5 dB over Splatt3R; StructSplat (ECCV 2026)
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  ├── Surgical GS SLAM ──── Track2Map (MICCAI 2026)
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  │     └── Track-anchored deformation ──── Dense 2D point tracks → stable surgical GS SLAM
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  ├── Knowledge-Constrained Reconstruction ──── KDH-CAD [2606.01702], ASSEMCAD (ECCV 2026), ArtiTwinSplat
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  │     ├── Domain-constrained parametric fitting ──── Foundation model + textbook knowledge + 250 samples → 92.6% accuracy
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  │     ├── NL-driven CAD assembly ──── ASSEMCAD (ECCV 2026): natural language → production-ready assembly graph; LLM-driven part selection + constraint generation
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  │     └── Interactable digital twin ──── ArtiTwinSplat (RGB-D reconstruction; agent-driven articulated object manipulation)
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  ├── Mid-Surface Extraction ──── MidSurfNet [2606.01891]
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  │     ├── Neural face pairing ──── Replaces handcrafted geometric heuristics
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  │     └── CAE/FEA mid-surface ──── SDF intersection for arbitrary offset control
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  ├── VLM Procedural Generation ──── SEIG [2606.02580]
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  │     └── Image → Blender Python ──── Geometry → Materials → Composition → Lighting (editable, semantic, simulation-ready)
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  └── Dynamics Prediction ──── MRO-GWM [2606.01950]
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        ├── Canonical Gaussian per object ──── Spatio-temporal transformer predicts rigid body motion
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        └── Model-predictive control ──── Non-prehensile manipulation
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  ├── Provenance & IP Forensics ──── GaussTrace [arXiv:2606.10612] (ICML 2026)
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  │     ├── Evidence-driven LLM reasoning ──── Constructs directed provenance graphs from Gaussian scene attributes
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  │     └── 3DGS model IP protection ──── Traces model lineage, training data influence, and forgery detection

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

{
  "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"}]
    }
  ]
}