Fix CONDA_PREFIX env, add Open3D for GLB mesh export

app.py

@@ -21,6 +21,12 @@ from PIL import Image
 if os.environ.get("HF_TOKEN"):
     login(token=os.environ.get("HF_TOKEN"))
 
+# Set CUDA_HOME for sam-3d-objects (expects conda but we're not using it)
+if "CUDA_HOME" not in os.environ:
+    os.environ["CUDA_HOME"] = "/usr/local/cuda"
+if "CONDA_PREFIX" not in os.environ:
+    os.environ["CONDA_PREFIX"] = "/usr/local"
+
 # Clone sam-3d-objects repo if not exists
 SAM3D_PATH = Path("/home/user/app/sam-3d-objects")
 if not SAM3D_PATH.exists():
@@ -142,27 +148,47 @@ def reconstruct_objects(image: np.ndarray):
         if result is None:
             return None, preview, "⚠️ 3D reconstruction failed"
 
-        # Export as PLY (gaussian splat format)
+        # Export as GLB
         output_dir = tempfile.mkdtemp()
-        ply_path = f"{output_dir}/object_{uuid.uuid4().hex[:8]}.ply"
+        glb_path = f"{output_dir}/object_{uuid.uuid4().hex[:8]}.glb"
 
-        # Save the gaussian splat
+        # Extract point cloud from result and convert to mesh
         if hasattr(result, 'save_ply'):
+            # Save temp PLY then convert
+            ply_path = f"{output_dir}/temp.ply"
             result.save_ply(ply_path)
+            # Load and convert to mesh using Open3D
+            import open3d as o3d
+            pcd = o3d.io.read_point_cloud(ply_path)
+            # Estimate normals and create mesh via Poisson reconstruction
+            pcd.estimate_normals()
+            mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
+            o3d.io.write_triangle_mesh(glb_path, mesh)
         elif 'gaussians' in result:
+            ply_path = f"{output_dir}/temp.ply"
             result['gaussians'].save_ply(ply_path)
+            import open3d as o3d
+            pcd = o3d.io.read_point_cloud(ply_path)
+            pcd.estimate_normals()
+            mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
+            o3d.io.write_triangle_mesh(glb_path, mesh)
         else:
-            # Try to extract vertices and save as point cloud
+            # Try to extract vertices
             vertices = result.get('xyz', result.get('points', None))
             if vertices is not None:
                 if torch.is_tensor(vertices):
                     vertices = vertices.cpu().numpy()
-                cloud = trimesh.PointCloud(vertices)
-                cloud.export(ply_path)
+                # Create mesh from points
+                import open3d as o3d
+                pcd = o3d.geometry.PointCloud()
+                pcd.points = o3d.utility.Vector3dVector(vertices)
+                pcd.estimate_normals()
+                mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
+                o3d.io.write_triangle_mesh(glb_path, mesh)
             else:
                 return None, preview, "⚠️ Could not extract 3D data"
 
-        return ply_path, preview, f"✓ Detected {len(masks)} objects, reconstructed largest"
+        return glb_path, preview, f"✓ Detected {len(masks)} objects, reconstructed largest"
 
     except Exception as e:
         import traceback

requirements.txt

@@ -10,6 +10,7 @@ numpy>=1.26.0
 opencv-python>=4.8.0
 Pillow>=10.0.0
 trimesh>=4.0.0
+open3d>=0.18.0
 hydra-core>=1.3.0
 omegaconf>=2.3.0
 einops>=0.7.0