Replace SAM3 with SAM2 for automatic mask generation

app.py

@@ -2,7 +2,7 @@
 SAM 3D Objects MCP Server
 Image → 3D Object (GLB)
 
-Automatic object detection with SAM3 + 3D reconstruction with SAM 3D Objects.
+Automatic object detection with SAM2 + 3D reconstruction with SAM 3D Objects.
 """
 import os
 import sys
@@ -36,29 +36,24 @@ sys.path.insert(0, str(SAM3D_PATH))
 
 # Global models
 SAM3D_MODEL = None
-SAM3_GENERATOR = None
+SAM2_GENERATOR = None
 
 
-def load_sam3():
-    """Load SAM3 automatic mask generator"""
-    global SAM3_GENERATOR
+def load_sam2():
+    """Load SAM2 automatic mask generator"""
+    global SAM2_GENERATOR
 
-    if SAM3_GENERATOR is not None:
-        return SAM3_GENERATOR
+    if SAM2_GENERATOR is not None:
+        return SAM2_GENERATOR
 
-    import torch
-    from sam3.automatic_mask_generator import SAM3AutomaticMaskGenerator
-    from sam3.model_builder import build_sam3
-
-    print("Loading SAM3 model...")
+    from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
 
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print("Loading SAM2 model...")
 
-    sam3_model = build_sam3(device=device)
-    SAM3_GENERATOR = SAM3AutomaticMaskGenerator(sam3_model)
+    SAM2_GENERATOR = SAM2AutomaticMaskGenerator.from_pretrained("facebook/sam2-hiera-large")
 
-    print("✓ SAM3 loaded")
-    return SAM3_GENERATOR
+    print("✓ SAM2 loaded")
+    return SAM2_GENERATOR
 
 
 def load_sam3d():
@@ -105,7 +100,7 @@ def reconstruct_objects(image: np.ndarray):
         from PIL import Image as PILImage
 
         # Load models
-        generator = load_sam3()
+        generator = load_sam2()
         sam3d = load_sam3d()
 
         # Convert to PIL if needed

requirements.txt

@@ -20,6 +20,4 @@ jaxtyping
 rich
 kaolin==0.17.0
 gsplat
-sam3>=0.1.2
-open_clip_torch>=2.24.0
-ftfy
+sam2>=1.1.0