def METHOD_NAME(S1, S2):
    """Computes a similarity transform (sR, t) that takes
    a set of 3D points S1 (3 x N) closest to a set of 3D points S2,
    where R is an 3x3 rotation matrix, t 3x1 translation, s scale.
    i.e. solves the orthogonal Procrutes problem.
    """
    transposed = False
    if S1.shape[0] != 3 and S1.shape[0] != 2:
        S1 = S1.T
        S2 = S2.T
        transposed = True
    assert (S2.shape[1] == S1.shape[1])
    # 1. Remove mean.
    mu1 = S1.mean(axis=1, keepdims=True)
    mu2 = S2.mean(axis=1, keepdims=True)
    X1 = S1 - mu1
    X2 = S2 - mu2
    # 2. Compute variance of X1 used for scale.
    var1 = np.sum(X1**2)
    # 3. The outer product of X1 and X2.
    K = X1.dot(X2.T)
    # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are
    # singular vectors of K.
    U, s, Vh = np.linalg.svd(K)
    V = Vh.T
    # Construct Z that fixes the orientation of R to get det(R)=1.
    Z = np.eye(U.shape[0])
    Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T)))
    # Construct R.
    R = V.dot(Z.dot(U.T))
    # 5. Recover scale.
    scale = np.trace(R.dot(K)) / var1
    # 6. Recover translation.
    t = mu2 - scale * (R.dot(mu1))
    # 7. Error:
    S1_hat = scale * R.dot(S1) + t
    if transposed:
        S1_hat = S1_hat.T
    return S1_hat