# InteriorFusion Dataset Strategy

## Core Training Dataset: InteriorFusion-Train

We curate a composite dataset from multiple sources, processed into a unified format.

### Dataset Composition

| Source | Split | Rooms/Scenes | Images | Purpose | Weight |
|--------|-------|-------------|--------|---------|--------|
| 3D-FRONT (HF MIDI-3D) | train | 14,000 | ~500K | Primary training | 40% |
| Structured3D | train | 18,000 | ~360K | Layout structure | 25% |
| InteriorNet | train | 50,000 | ~1M | Scale pre-training | 20% |
| ScanNet++ | train | 1,200 | ~50K | Real-world adaptation | 10% |
| HM3D | train | 800 | ~30K | Real-world adaptation | 5% |

**Total: ~85K rooms, ~2M training images**

### Unified Data Format

```python
@dataclass
class InteriorSample:
    # Input
    image: torch.Tensor           # [3, H, W] — single interior photo
    depth: torch.Tensor           # [1, H, W] — metric depth in meters
    normal: torch.Tensor          # [3, H, W] — surface normals
    
    # Scene understanding
    room_layout: RoomLayout       # Walls, floor, ceiling planes
    room_type: str                # "living_room", "bedroom", "kitchen"
    style: str                    # "modern", "scandinavian", "luxury"
    scene_graph: SceneGraph       # Object nodes + spatial relations
    
    # Per-object data
    objects: List[ObjectData]     # Individual furniture items
    
    # 3D ground truth
    room_mesh: trimesh.Trimesh     # Full room mesh (walls + floor + ceiling)
    object_meshes: List[trimesh.Trimesh]  # Per-object meshes
    gaussian_cloud: GaussianCloud  # 3D Gaussian representation
    
    # Materials
    materials: List[PBRMaterial]   # Per-object PBR materials
    wall_material: PBRMaterial
    floor_material: PBRMaterial
    
    # Camera
    camera_pose: CameraPose       # Intrinsics + extrinsics
    fov: float
    
    # Metadata
    source: str                   # "3dfront", "structured3d", "scannet"
    caption: str                  # Natural language description
```

### Preprocessing Pipeline

```
Raw Dataset → Filter → Render Views → Compute Depth →
    Segment Objects → Extract Layout →
    Generate Multi-View → Create SLAT →
    Validate → Package → Upload to HF
```

### Filtering Criteria

1. **Quality filter**: Minimum resolution 512×512
2. **Content filter**: Must contain at least 2 furniture objects
3. **Occlusion filter**: Main objects must be >30% visible
4. **Room type filter**: Exclude bathrooms, garages, outdoor
5. **Lighting filter**: Exclude extremely dark or overexposed scenes
6. **Duplicate filter**: Perceptual hash deduplication

### Augmentation Pipeline

1. **Color jitter**: brightness ±0.2, contrast ±0.2, saturation ±0.2, hue ±0.1
2. **Random crop**: 0.8–1.0 scale, maintain aspect ratio
3. **Horizontal flip**: 50% probability
4. **Perspective warp**: Simulate different camera angles (±15° pitch, ±20° yaw)
5. **Synthetic occlusion**: Add random rectangles simulating foreground objects
6. **Depth noise**: Add Gaussian noise to depth map (σ=0.05m) for robustness
7. **Lighting variation**: Re-render with different HDRI environments

### Captioning Strategy

**Automatic captions** from Cap3D-style generation:
- Room type: "a modern living room with a gray sofa and wooden coffee table"
- Style: "scandinavian minimalist interior with natural light"
- Objects: "contains: sofa, coffee table, floor lamp, bookshelf"
- Materials: "wooden floor, white walls, leather sofa"
- Spatial: "sofa against back wall, coffee table centered, lamp in corner"

**Manual review**: 10% random sample reviewed by interior designers for quality.

### Synthetic Data Generation

Using ProcTHOR + AI2-THOR simulator:
1. Generate 100K additional procedural rooms
2. Randomize: furniture placement, materials, lighting, camera position
3. Render 20 views per room
4. Add to training mix with 15% weight

### Data Splits

| Split | Rooms | Images | Purpose |
|-------|-------|--------|---------|
| Train | 75,000 | 1,800,000 | Model training |
| Val | 5,000 | 120,000 | Hyperparameter tuning |
| Test | 5,000 | 120,000 | Final evaluation |
| Benchmark | 500 | 12,000 | Leaderboard / comparison |