def closest_point_naive(mesh, points):
    """
    Given a mesh and a list of points find the closest point
    on any triangle.

    Does this by constructing a very large intermediate array and
    comparing every point to every triangle.

    Parameters
    ----------
    mesh : Trimesh
      Takes mesh to have same interfaces as `closest_point`
    points : (m, 3) float
      Points in space

    Returns
    ----------
    closest : (m, 3) float
      Closest point on triangles for each point
    distance : (m,) float
      Distances between point and triangle
    triangle_id : (m,) int
      Index of triangle containing closest point
    """
    # get triangles from mesh
    triangles = mesh.triangles.view(np.ndarray)
    # establish that input points are sane
    points = np.asanyarray(points, dtype=np.float64)
    if not util.is_shape(triangles, (-1, 3, 3)):
        raise ValueError('triangles shape incorrect')
    if not util.is_shape(points, (-1, 3)):
        raise ValueError('points must be (n,3)')

    # create a giant tiled array of each point tiled len(triangles) times
    points_tiled = np.tile(points, (1, len(triangles)))
    on_triangle = np.array([closest_point_corresponding(
        triangles, i.reshape((-1, 3))) for i in points_tiled])

    # distance squared
    distance_2 = [((i - q)**2).sum(axis=1)
                  for i, q in zip(on_triangle, points)]

    triangle_id = np.array([i.argmin() for i in distance_2])

    # closest cartesian point
    closest = np.array([g[i] for i, g in zip(triangle_id, on_triangle)])
    distance = np.array([g[i] for i, g in zip(triangle_id, distance_2)]) ** .5

    return closest, distance, triangle_id