Details about sampling from 3d objects #8

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1. Quest4Science commented on Jun 25, 2025

   

   Quest4Science

   on Jun 25, 2025

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   Could you please clarify how your pipeline handles non-watertight (open) meshes during tetrahedralization?

   Do you perform any mesh repair or hole filling before running TetGen?

   If TetGen fails on meshes with holes, do you have a fallback strategy (e.g., using only surface points for sampling)?

   Or does your implementation rely on TetGen's internal handling of such cases?
2. Colin97 commented on Jul 16, 2025

   

   Colin97

   on Jul 16, 2025

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   def compute(vertices: np.ndarray, faces: np.ndarray, size: int = 128,
               fix: bool = False, level: float = 0.015, return_mesh: bool = False):
       r""" Converts a input mesh to signed distance field (SDF).
   
       Args:
         vertices (np.ndarray): The vertices of the input mesh, the vertices MUST be
             in range [-1, 1].
         faces (np.ndarray): The faces of the input mesh.
         size (int): The resolution of the resulting SDF.
         fix (bool): If the input mesh is not watertight, set :attr:`fix` as True.
         level (float): The value used to extract level sets when :attr:`fix` is True,
             with a default value of 0.015 (as a reference 2/128 = 0.015625). And the
             recommended default value is 2/size.
         return_mesh (bool): If True, also return the fixed mesh.
       """
       # compute sdf
       sdf = mesh2sdf.core.compute(vertices, faces, size)
       if not fix:
           return (sdf, trimesh.Trimesh(vertices, faces)) if return_mesh else sdf
   
       # NOTE: the negative value is not reliable if the mesh is not watertight
       udf = np.abs(sdf)
       vertices, faces, _, _ = skimage.measure.marching_cubes(udf, level)
       components = trimesh.Trimesh(vertices, faces).split(only_watertight=False)
       new_mesh = [] #trimesh.Trimesh()
       if len(components) > 100000:
           raise NotImplementedError
       for i, c in enumerate(components):
           c.fix_normals()
           new_mesh.append(c) #trimesh.util.concatenate(new_mesh, c)
       new_mesh = trimesh.util.concatenate(new_mesh)
   
       # generate tet mesh
       tet = tetgen.TetGen(new_mesh.vertices, new_mesh.faces)
       tet.tetrahedralize(plc=True, nobisect=1., quality=True, fixedvolume=True, maxvolume=math.sqrt(2) / 12 * (2 / size) ** 3)
       tmp_vtk = tempfile.NamedTemporaryFile(suffix='.vtk', delete=True)
       tet.grid.save(tmp_vtk.name)
   
       reader = vtk.vtkUnstructuredGridReader()
       reader.SetFileName(tmp_vtk.name)
       reader.Update()
       
       # extract tet vertices (surface & internal)
       unstructuredGrid = reader.GetOutput()
       points = unstructuredGrid.GetPoints()
       vtk_points = points.GetData()
       tet_points = np.array(numpy_support.vtk_to_numpy(vtk_points)) * (2.0 / size) - 1.0
       
       # extract surface mesh from tet mesh
       surface_filter = vtk.vtkDataSetSurfaceFilter()
       surface_filter.SetInputConnection(reader.GetOutputPort())
       surface_filter.Update()
       polydata = surface_filter.GetOutput()
       writer = vtk.vtkOBJWriter()
       tmp_obj = tempfile.NamedTemporaryFile(suffix='.obj', delete=True)
       writer.SetFileName(tmp_obj.name)
       writer.SetInputData(polydata)
       writer.Update()
   
       # recompute sdf value
       new_mesh = trimesh.load(tmp_obj.name, force='mesh')
       new_mesh.vertices = new_mesh.vertices * (2.0 / size) - 1.0  # normalize it to [-1, 1]
       sdf = mesh2sdf.core.compute(new_mesh.vertices, new_mesh.faces, size)
       return (sdf, new_mesh, tet_points) if return_mesh else sdf
   

   Here is the code snippet for processing the PartNet dataset and generating interior point triplets. Hope it helps. Yes, we perform some preprocessing before tetrahedralization (see the code for details). In our implementation, the triplet points are sampled from the tetrahedral mesh vertices; we did not sample additional points from the interior of each tetrahedron.