Initial SAM 3D Objects MCP server

README.md

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+---
+title: SAM 3D Objects MCP
+emoji: 📦
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 6.0.2
+python_version: "3.10"
+app_file: app.py
+pinned: false
+license: other
+hardware: zero-a10g
+tags:
+  - mcp-server-track
+  - building-mcp-track-consumer
+  - agents
+  - 3d
+  - object-reconstruction
+  - sam3d
+short_description: "Image + Mask → 3D Object (PLY) - MCP Server"
+---
+
+# 📦 SAM 3D Objects MCP Server
+
+**Image + Mask → 3D Object** powered by [Meta's SAM 3D Objects](https://github.com/facebookresearch/sam-3d-objects)
+
+## MCP Integration
+
+```json
+{
+  "mcpServers": {
+    "sam3d-objects": {
+      "command": "npx",
+      "args": ["mcp-remote", "https://dev-bjoern-sam3d-objects-mcp.hf.space/gradio_api/mcp/sse"]
+    }
+  }
+}
+```
+
+## Credits
+- [facebook/sam-3d-objects](https://huggingface.co/facebook/sam-3d-objects)
+- [facebookresearch/sam-3d-objects](https://github.com/facebookresearch/sam-3d-objects)

app.py

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+"""
+SAM 3D Objects MCP Server
+Image + Mask → 3D Object (PLY)
+"""
+import os
+import sys
+import subprocess
+import tempfile
+import uuid
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+import spaces
+from huggingface_hub import snapshot_download, login
+from PIL import Image
+
+# Login with HF_TOKEN if available
+if os.environ.get("HF_TOKEN"):
+    login(token=os.environ.get("HF_TOKEN"))
+
+# Clone sam-3d-objects repo if not exists
+SAM3D_PATH = Path("/home/user/app/sam-3d-objects")
+if not SAM3D_PATH.exists():
+    print("Cloning sam-3d-objects repository...")
+    subprocess.run([
+        "git", "clone",
+        "https://github.com/facebookresearch/sam-3d-objects.git",
+        str(SAM3D_PATH)
+    ], check=True)
+    sys.path.insert(0, str(SAM3D_PATH))
+
+# Add to path
+sys.path.insert(0, str(SAM3D_PATH))
+
+# Global model
+MODEL = None
+
+
+def load_model():
+    """Load SAM 3D Objects model"""
+    global MODEL
+
+    if MODEL is not None:
+        return MODEL
+
+    import torch
+    print("Loading SAM 3D Objects model...")
+
+    # Download checkpoint
+    checkpoint_dir = snapshot_download(
+        repo_id="facebook/sam-3d-objects",
+        token=os.environ.get("HF_TOKEN")
+    )
+
+    from sam_3d_objects import Sam3dObjects
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    MODEL = Sam3dObjects.from_pretrained(checkpoint_dir, device=device)
+
+    print("✓ Model loaded")
+    return MODEL
+
+
+@spaces.GPU(duration=120)
+def reconstruct_object(image: np.ndarray, mask: np.ndarray) -> tuple:
+    """
+    Reconstruct 3D object from image and mask.
+
+    Args:
+        image: Input RGB image
+        mask: Binary mask indicating object region
+
+    Returns:
+        tuple: (ply_path, status)
+    """
+    if image is None:
+        return None, "❌ No image provided"
+    if mask is None:
+        return None, "❌ No mask provided"
+
+    try:
+        import torch
+        import trimesh
+        model = load_model()
+
+        # Process image
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+
+        # Process mask
+        if isinstance(mask, Image.Image):
+            mask = np.array(mask)
+
+        # Convert mask to binary if needed
+        if len(mask.shape) == 3:
+            mask = mask[:, :, 0]
+        mask = (mask > 127).astype(np.uint8)
+
+        # Run inference
+        outputs = model.predict(image, mask)
+
+        if outputs is None:
+            return None, "⚠️ Reconstruction failed"
+
+        # Export as PLY
+        output_dir = tempfile.mkdtemp()
+        ply_path = f"{output_dir}/object_{uuid.uuid4().hex[:8]}.ply"
+
+        # Save gaussian splat as PLY
+        outputs.save_ply(ply_path)
+
+        return ply_path, "✓ Object reconstructed"
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return None, f"❌ Error: {e}"
+
+
+# Gradio Interface
+with gr.Blocks(title="SAM 3D Objects MCP") as demo:
+    gr.Markdown("# 📦 SAM 3D Objects MCP Server\n**Image + Mask → 3D Object (PLY)**")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="numpy")
+            input_mask = gr.Image(label="Object Mask", type="numpy")
+            btn = gr.Button("🎯 Reconstruct", variant="primary")
+
+        with gr.Column():
+            output_file = gr.File(label="3D Object (PLY)")
+            status = gr.Textbox(label="Status")
+
+    btn.click(reconstruct_object, inputs=[input_image, input_mask], outputs=[output_file, status])
+
+    gr.Markdown("""
+    ---
+    ### MCP Server
+    ```json
+    {"mcpServers": {"sam3d-objects": {"command": "npx", "args": ["mcp-remote", "URL/gradio_api/mcp/sse"]}}}
+    ```
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

requirements.txt

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+torch>=2.5.0
+torchvision>=0.20.0
+gradio>=6.0.2
+huggingface_hub>=0.26.0
+spaces>=0.30.0
+kaolin==0.17.0
+gsplat @ git+https://github.com/nerfstudio-project/gsplat.git@2323de5905d5e90e035f792fe65bad0fedd413e7
+seaborn==0.13.2
+numpy>=1.26.0
+opencv-python>=4.8.0
+Pillow>=10.0.0
+trimesh>=4.0.0
+hydra-core>=1.3.0
+omegaconf>=2.3.0
+einops>=0.7.0
+loguru>=0.7.0