Added 3D mesh cleanup to remove artifacts v1

app.py

@@ -57,14 +57,6 @@ def resize_image(image_path, max_size=1024):
 def generate_3d_model(depth, image_path, focallength_px):
     """
     Generate a textured 3D mesh from the depth map and the original image.
-
-    Args:
-        depth (np.ndarray): 2D array representing depth in meters.
-        image_path (str): Path to the RGB image.
-        focallength_px (float): Focal length in pixels.
-
-    Returns:
-        tuple: Paths to the exported 3D model files for viewing and downloading.
     """
     # Load the RGB image and convert to a NumPy array
     image = np.array(Image.open(image_path))
@@ -116,6 +108,28 @@ def generate_3d_model(depth, image_path, focallength_px):
     # Create the mesh using Trimesh with vertex colors
     mesh = trimesh.Trimesh(vertices=vertices, faces=faces, vertex_colors=colors)
 
+    # Mesh cleaning and improvement steps
+    print("Original mesh - vertices: {}, faces: {}".format(len(mesh.vertices), len(mesh.faces)))
+
+    # 1. Mesh simplification
+    mesh = mesh.simplify_quadratic_decimation(target_percent=0.8)
+    print("After simplification - vertices: {}, faces: {}".format(len(mesh.vertices), len(mesh.faces)))
+
+    # 2. Remove small disconnected components
+    components = mesh.split(only_watertight=False)
+    if len(components) > 1:
+        areas = np.array([c.area for c in components])
+        mesh = components[np.argmax(areas)]
+        print("After removing small components - vertices: {}, faces: {}".format(len(mesh.vertices), len(mesh.faces)))
+
+    # 3. Smooth the mesh
+    mesh = mesh.smoothed()
+    print("After smoothing - vertices: {}, faces: {}".format(len(mesh.vertices), len(mesh.faces)))
+
+    # 4. Remove thin features
+    mesh = remove_thin_features(mesh)
+    print("After removing thin features - vertices: {}, faces: {}".format(len(mesh.vertices), len(mesh.faces)))
+
     # Export the mesh to OBJ files with unique filenames
     timestamp = int(time.time())
     view_model_path = f'view_model_{timestamp}.obj'
@@ -124,6 +138,30 @@ def generate_3d_model(depth, image_path, focallength_px):
     mesh.export(download_model_path)
     return view_model_path, download_model_path
 
+def remove_thin_features(mesh, thickness_threshold=0.01):
+    """
+    Remove thin features from the mesh.
+    """
+    # Calculate edge lengths
+    edges = mesh.edges_unique
+    edge_points = mesh.vertices[edges]
+    edge_lengths = np.linalg.norm(edge_points[:, 0] - edge_points[:, 1], axis=1)
+    
+    # Identify short edges
+    short_edges = edges[edge_lengths < thickness_threshold]
+    
+    # Collapse short edges
+    for edge in short_edges:
+        try:
+            mesh.collapse_edge(edge)
+        except:
+            pass  # Skip if edge collapse fails
+    
+    # Remove any newly created degenerate faces
+    mesh.remove_degenerate_faces()
+    
+    return mesh
+
 @spaces.GPU(duration=20)
 def predict_depth(input_image):
     temp_file = None