code: def remove_backface(
      mesh,
      pc,
      k=3,
      lap_iter=50,
      lap_val=0.50,
      area_threshold=1,
      verbose=False,
  ):
      """Remove the interior backface resulting from reconstruction artifacts.
  
      Args:
        mesh: trimesh instance. mesh recon. from lig that may contain backface.
        pc: np.array of shape [n, 6], original input point cloud.
        k: int, number of nearest neighbor for pooling sign.
        lap_iter: int, number of laplacian smoothing iterations.
        lap_val: float, lambda value for laplacian smoothing of cosine distance.
        area_threshold: float, minimum area connected components to preserve.
        verbose: bool, verbose print.
      Returns:
        mesh_new: trimesh instance. new mesh with backface removed.
      """
      mesh.remove_degenerate_faces()
  
      v, n = pc[:, :3], pc[:, 3:]
  
      # build cKDTree to accelerate nearest point search
      if verbose:
          print("Building KDTree...")
      tree_pc = spatial.cKDTree(data=v)
  
      # for each vertex, find nearest point in input point cloud
      if verbose:
          print("{}-nearest neighbor search...".format(k))
      _, idx = tree_pc.query(mesh.vertices, k=k, n_jobs=-1)
  
      # slice out the nn points
      n_nn = n[idx]  # shape: [#v_query, k, dim]
  
      # find dot products.
      if verbose:
          print("Computing norm alignment...")
      n_v = mesh.vertex_normals[:, None, :]  # shape: [#v_query, 1, dim]
      per_vert_norm_alignment = np.sum(
          n_nn * n_v, axis=-1
      )  # [#v_query, k]
      per_vert_norm_alignment = np.mean(
          per_vert_norm_alignment, axis=-1
      )
  
      # laplacian smoothing of per vertex normal alignment
      if verbose:
          print("Computing laplacian smoothing...")
      lap = trimesh.smoothing.laplacian_calculation(mesh)
      dlap = lap.shape[0]
      op = sparse.eye(dlap) + lap_val * (lap - sparse.eye(dlap))
      for _ in range(lap_iter):
          per_vert_norm_alignment = op.dot(per_vert_norm_alignment)
  
      # average onto face
      per_face_norm_alignment = average_onto_face(
          mesh, per_vert_norm_alignment
      )
  
      # remove faces with per_face_norm_alignment < 0
      if verbose:
          print("Removing backfaces...")
      ff = mesh.faces[per_face_norm_alignment > -0.75]
      mesh_new = trimesh.Trimesh(mesh.vertices, ff)
      mesh_new.remove_unreferenced_vertices()
  
      if verbose:
          print("Cleaning up...")
      mesh_list = mesh_new.split(only_watertight=False)
      # filter out small floating junk from backface
      areas = [m.area for m in mesh_list]
      threshold = min(np.max(areas) / 5, area_threshold)
      mesh_list = [m for m in mesh_list if m.area > threshold]
      mesh_new = merge_meshes(mesh_list)
  
      # fill small holes
      mesh_new.fill_holes()
  
      return mesh_new
  
imports: import numpy
  from scipy import sparse
  from scipy import spatial
  import trimesh
label_desc: Use image node_id 4 for calling a global function with example usage: remove_backface(mesh, pc, k, lap_iter, lap_val, area_threshold, verbose) and returns: mesh_new