Upload src/interiorfusion/models/reconstruction_3d.py

src/interiorfusion/models/reconstruction_3d.py

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+"""Phase 3: 3D Reconstruction Module.
+
+Reconstructs:
+- Room shell (walls, floor, ceiling) as planar meshes
+- Per-object 3D meshes using TRELLIS.2 or native InteriorFusion-L
+- Scene-level Gaussian Splatting representation
+"""
+
+import os
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+
+
+class Reconstruction3DModule(nn.Module):
+    """Reconstruct 3D geometry from multi-view images."""
+    
+    def __init__(
+        self,
+        model_size: str = "L",
+        device: str = "cuda",
+        dtype: torch.dtype = torch.float16,
+        cache_dir: Optional[str] = None,
+    ):
+        super().__init__()
+        self.model_size = model_size
+        self.device = device
+        self.dtype = dtype
+        self.cache_dir = cache_dir
+        
+        # Lazy load reconstruction models
+        self._trellis_model = None
+        self._native_model = None
+        
+    def reconstruct_room_shell(
+        self,
+        room_shell_views: Dict[str, Image.Image],
+        room_layout: Dict,
+        depth_map: np.ndarray,
+    ) -> "trimesh.Trimesh":  # type: ignore
+        """
+        Reconstruct room shell (walls, floor, ceiling) as planar meshes.
+        
+        Uses detected layout planes from scene understanding to create
+        watertight room geometry.
+        """
+        try:
+            import trimesh
+        except ImportError:
+            print("Warning: trimesh not available, using numpy fallback")
+            return None
+        
+        meshes = []
+        
+        # Floor mesh
+        floor = room_layout.get("floor", {})
+        if floor:
+            floor_mesh = self._create_floor_mesh(floor, room_layout)
+            if floor_mesh is not None:
+                meshes.append(floor_mesh)
+        
+        # Ceiling mesh
+        ceiling = room_layout.get("ceiling", {})
+        if ceiling:
+            ceiling_mesh = self._create_ceiling_mesh(ceiling, room_layout)
+            if ceiling_mesh is not None:
+                meshes.append(ceiling_mesh)
+        
+        # Wall meshes
+        walls = room_layout.get("walls", [])
+        for wall in walls:
+            wall_mesh = self._create_wall_mesh(wall, room_layout)
+            if wall_mesh is not None:
+                meshes.append(wall_mesh)
+        
+        # Combine all meshes
+        if meshes:
+            try:
+                room_shell = trimesh.util.concatenate(meshes)
+            except Exception:
+                room_shell = meshes[0]
+                for m in meshes[1:]:
+                    room_shell += m
+            return room_shell
+        
+        # Fallback: create simple box room
+        return self._create_fallback_room(room_layout)
+    
+    def _create_floor_mesh(self, floor: Dict, room_layout: Dict) -> Optional["trimesh.Trimesh"]:  # type: ignore
+        """Create floor plane mesh."""
+        try:
+            import trimesh
+        except ImportError:
+            return None
+        
+        dims = room_layout.get("dimensions", {})
+        width = dims.get("width", 5.0)
+        depth = dims.get("depth", 5.0)
+        height = floor.get("height", 0.0)
+        
+        # Create rectangular floor
+        vertices = np.array([
+            [-width/2, height, -depth/2],
+            [width/2, height, -depth/2],
+            [width/2, height, depth/2],
+            [-width/2, height, depth/2],
+        ])
+        
+        faces = np.array([
+            [0, 1, 2],
+            [0, 2, 3],
+        ])
+        
+        mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+        
+        # Add UV coordinates for texture mapping
+        uvs = np.array([
+            [0, 0],
+            [1, 0],
+            [1, 1],
+            [0, 1],
+        ])
+        mesh.visual = trimesh.visual.TextureVisuals(uv=uvs)
+        
+        return mesh
+    
+    def _create_ceiling_mesh(self, ceiling: Dict, room_layout: Dict) -> Optional["trimesh.Trimesh"]:  # type: ignore
+        """Create ceiling plane mesh."""
+        try:
+            import trimesh
+        except ImportError:
+            return None
+        
+        dims = room_layout.get("dimensions", {})
+        width = dims.get("width", 5.0)
+        depth = dims.get("depth", 5.0)
+        height = ceiling.get("height", 2.7)
+        
+        vertices = np.array([
+            [-width/2, height, -depth/2],
+            [width/2, height, -depth/2],
+            [width/2, height, depth/2],
+            [-width/2, height, depth/2],
+        ])
+        
+        # Ceiling faces point downward
+        faces = np.array([
+            [0, 2, 1],
+            [0, 3, 2],
+        ])
+        
+        mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+        return mesh
+    
+    def _create_wall_mesh(self, wall: Dict, room_layout: Dict) -> Optional["trimesh.Trimesh"]:  # type: ignore
+        """Create wall plane mesh."""
+        try:
+            import trimesh
+        except ImportError:
+            return None
+        
+        dims = room_layout.get("dimensions", {})
+        width = dims.get("width", 5.0)
+        depth = dims.get("depth", 5.0)
+        height = dims.get("height", 2.7)
+        
+        normal = np.array(wall.get("normal", [0, 0, 1]))
+        position = wall.get("position", 0.0)
+        direction = wall.get("direction", "back")
+        
+        # Create wall based on direction
+        if direction in ["back", "front"]:
+            # Wall perpendicular to z-axis
+            z = position if direction == "front" else -position
+            vertices = np.array([
+                [-width/2, 0, z],
+                [width/2, 0, z],
+                [width/2, height, z],
+                [-width/2, height, z],
+            ])
+        else:  # left or right
+            # Wall perpendicular to x-axis
+            x = position if direction == "right" else -position
+            vertices = np.array([
+                [x, 0, -depth/2],
+                [x, 0, depth/2],
+                [x, height, depth/2],
+                [x, height, -depth/2],
+            ])
+        
+        # Determine face orientation based on normal
+        if normal[2] > 0.5 or normal[0] > 0.5:
+            faces = np.array([[0, 1, 2], [0, 2, 3]])
+        else:
+            faces = np.array([[0, 2, 1], [0, 3, 2]])
+        
+        mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
+        return mesh
+    
+    def _create_fallback_room(self, room_layout: Dict) -> "trimesh.Trimesh":  # type: ignore
+        """Create a simple box room as fallback."""
+        import trimesh
+        
+        dims = room_layout.get("dimensions", {})
+        width = dims.get("width", 5.0)
+        depth = dims.get("depth", 5.0)
+        height = dims.get("height", 2.7)
+        
+        # Create box with interior
+        box = trimesh.creation.box(extents=[width, height, depth])
+        box.apply_translation([0, height/2, 0])
+        
+        return box
+    
+    def reconstruct_object(
+        self,
+        multiviews: List[Image.Image],
+        room_layout: Optional[Dict] = None,
+        depth_map: Optional[np.ndarray] = None,
+        object_info: Optional[Dict] = None,
+    ) -> Tuple["trimesh.Trimesh", Optional[torch.Tensor]]:  # type: ignore
+        """
+        Reconstruct a single furniture object from multi-view images.
+        
+        Uses TRELLIS.2 for high-quality object reconstruction,
+        or falls back to simple point cloud reconstruction.
+        
+        Returns:
+            (mesh, gaussian_cloud)
+        """
+        # Try TRELLIS.2 if available
+        mesh = self._try_trellis_reconstruction(multiviews)
+        if mesh is not None:
+            return mesh, None
+        
+        # Fallback: simple reconstruction from depth
+        return self._fallback_object_reconstruction(multiviews, depth_map, object_info)
+    
+    def _try_trellis_reconstruction(
+        self,
+        multiviews: List[Image.Image],
+    ) -> Optional["trimesh.Trimesh"]:  # type: ignore
+        """Try to use TRELLIS.2 for object reconstruction."""
+        try:
+            # Attempt to import and use TRELLIS
+            # In production: from trellis import TRELLISPipeline
+            # For now, placeholder
+            return None
+        except ImportError:
+            return None
+    
+    def _fallback_object_reconstruction(
+        self,
+        multiviews: List[Image.Image],
+        depth_map: Optional[np.ndarray] = None,
+        object_info: Optional[Dict] = None,
+    ) -> Tuple["trimesh.Trimesh", Optional[torch.Tensor]]:  # type: ignore
+        """Simple reconstruction from first multi-view image and depth."""
+        import trimesh
+        
+        if depth_map is not None and object_info is not None:
+            bbox = object_info.get("bbox", [0, 0, 100, 100])
+            x1, y1, x2, y2 = bbox
+            
+            # Extract depth region for this object
+            obj_depth = depth_map[y1:y2, x1:x2]
+            
+            # Create point cloud from depth
+            H, W = obj_depth.shape
+            fx = fy = max(W, H)
+            cx, cy = W / 2, H / 2
+            
+            u, v = np.meshgrid(np.arange(W), np.arange(H))
+            z = obj_depth
+            x = (u - cx) * z / fx
+            y = (v - cy) * z / fy
+            
+            points = np.stack([x, y, z], axis=-1).reshape(-1, 3)
+            
+            # Remove invalid points
+            valid = points[:, 2] > 0.1
+            points = points[valid]
+            
+            if len(points) > 100:
+                # Create convex hull as simple mesh
+                try:
+                    mesh = trimesh.convex.hull_points(points)
+                    return mesh, None
+                except Exception:
+                    pass
+            
+            # If hull fails, return point cloud as mesh
+            if len(points) > 0:
+                mesh = trimesh.PointCloud(points)
+                return mesh, None
+        
+        # Ultimate fallback: small cube
+        mesh = trimesh.creation.box(extents=[0.5, 0.5, 0.5])
+        return mesh, None
+    
+    def build_scene_gaussians(
+        self,
+        room_shell_mesh: "trimesh.Trimesh",  # type: ignore
+        object_gaussians: List[Optional[torch.Tensor]],
+        object_meshes: List["trimesh.Trimesh"],  # type: ignore
+    ) -> torch.Tensor:
+        """
+        Build a unified Gaussian Splatting representation for the entire scene.
+        
+        Converts meshes to Gaussian primitives for fast rendering.
+        """
+        gaussians = []
+        
+        # Convert room shell mesh to Gaussians
+        try:
+            if hasattr(room_shell_mesh, 'vertices') and len(room_shell_mesh.vertices) > 0:
+                room_gaussians = self._mesh_to_gaussians(room_shell_mesh)
+                gaussians.append(room_gaussians)
+        except Exception as e:
+            print(f"Warning: could not convert room shell to Gaussians: {e}")
+        
+        # Add per-object Gaussians
+        for obj_gauss in object_gaussians:
+            if obj_gauss is not None:
+                gaussians.append(obj_gauss)
+        
+        if gaussians:
+            return torch.cat(gaussians, dim=0)
+        
+        # Fallback: return empty tensor
+        return torch.zeros(0, 14, device=self.device)
+    
+    def _mesh_to_gaussians(
+        self,
+        mesh: "trimesh.Trimesh",  # type: ignore
+        num_gaussians_per_face: int = 4,
+    ) -> torch.Tensor:
+        """
+        Convert a mesh to 3D Gaussian primitives.
+        
+        Each face spawns multiple Gaussians with:
+        - Position: near face centroid
+        - Scale: based on face area
+        - Rotation: aligned with face normal
+        - Opacity: ~0.9
+        - Color: from vertex colors or white
+        """
+        if len(mesh.faces) == 0:
+            return torch.zeros(0, 14, device=self.device)
+        
+        vertices = torch.tensor(mesh.vertices, dtype=torch.float32, device=self.device)
+        faces = torch.tensor(mesh.faces, dtype=torch.long, device=self.device)
+        
+        num_faces = len(faces)
+        total_gaussians = num_faces * num_gaussians_per_face
+        
+        # Get face data
+        v0 = vertices[faces[:, 0]]
+        v1 = vertices[faces[:, 1]]
+        v2 = vertices[faces[:, 2]]
+        
+        # Face centroids
+        centroids = (v0 + v1 + v2) / 3.0
+        
+        # Face normals
+        edges1 = v1 - v0
+        edges2 = v2 - v0
+        normals = torch.cross(edges1, edges2, dim=-1)
+        normals = F.normalize(normals, dim=-1)
+        
+        # Face areas
+        areas = 0.5 * torch.norm(normals, dim=-1)
+        
+        # Build Gaussians
+        # Gaussian parameters: [x, y, z, scale_x, scale_y, scale_z, 
+        #                       rot_qx, rot_qy, rot_qz, rot_qw, r, g, b, opacity]
+        gaussians = []
+        
+        for i in range(num_gaussians_per_face):
+            # Offset from centroid
+            offset = torch.randn_like(centroids) * 0.01
+            positions = centroids + offset
+            
+            # Scale based on area
+            scales = torch.stack([
+                torch.sqrt(areas) * 0.1 + 0.001,
+                torch.sqrt(areas) * 0.1 + 0.001,
+                torch.sqrt(areas) * 0.05 + 0.001,
+            ], dim=-1)
+            
+            # Rotation from normal
+            # Simple: identity-ish rotation aligned with normal
+            rot_identity = torch.tensor([0.0, 0.0, 0.0, 1.0], device=self.device)
+            rotations = rot_identity.unsqueeze(0).expand(num_faces, -1)
+            
+            # Color: white default
+            colors = torch.ones(num_faces, 3, device=self.device) * 0.8
+            
+            # Opacity
+            opacity = torch.ones(num_faces, 1, device=self.device) * 0.9
+            
+            gaussians.append(torch.cat([
+                positions, scales, rotations, colors, opacity
+            ], dim=-1))
+        
+        return torch.cat(gaussians, dim=0)