Chamfer distance between two point clouds in tensorflow

I've implemented TF version of chamfer distance:

def distance_matrix(array1, array2):    """    arguments:         array1: the array, size: (num_point, num_feature)        array2: the samples, size: (num_point, num_feature)    returns:        distances: each entry is the distance from a sample to array1            , it's size: (num_point, num_point)    """    num_point, num_features = array1.shape    expanded_array1 = tf.tile(array1, (num_point, 1))    expanded_array2 = tf.reshape(            tf.tile(tf.expand_dims(array2, 1),                     (1, num_point, 1)),            (-1, num_features))    distances = tf.norm(expanded_array1-expanded_array2, axis=1)    distances = tf.reshape(distances, (num_point, num_point))    return distancesdef av_dist(array1, array2):    """    arguments:        array1, array2: both size: (num_points, num_feature)    returns:        distances: size: (1,)    """    distances = distance_matrix(array1, array2)    distances = tf.reduce_min(distances, axis=1)    distances = tf.reduce_mean(distances)    return distancesdef av_dist_sum(arrays):    """    arguments:        arrays: array1, array2    returns:        sum of av_dist(array1, array2) and av_dist(array2, array1)    """    array1, array2 = arrays    av_dist1 = av_dist(array1, array2)    av_dist2 = av_dist(array2, array1)    return av_dist1+av_dist2def chamfer_distance_tf(array1, array2):    batch_size, num_point, num_features = array1.shape    dist = tf.reduce_mean(               tf.map_fn(av_dist_sum, elems=(array1, array2), dtype=tf.float64)           )    return dist

And for validation purpose, I also implemented a sklearn version:

def chamfer_distance_sklearn(array1,array2):    batch_size, num_point = array1.shape[:2]    dist = 0    for i in range(batch_size):        tree1 = KDTree(array1[i], leaf_size=num_point+1)        tree2 = KDTree(array2[i], leaf_size=num_point+1)        distances1, _ = tree1.query(array2[i])        distances2, _ = tree2.query(array1[i])        av_dist1 = np.mean(distances1)        av_dist2 = np.mean(distances2)        dist = dist + (av_dist1+av_dist2)/batch_size    return dist

Also a numpy version:

def array2samples_distance(array1, array2):    """    arguments:         array1: the array, size: (num_point, num_feature)        array2: the samples, size: (num_point, num_feature)    returns:        distances: each entry is the distance from a sample to array1     """    num_point, num_features = array1.shape    expanded_array1 = np.tile(array1, (num_point, 1))    expanded_array2 = np.reshape(            np.tile(np.expand_dims(array2, 1),                     (1, num_point, 1)),            (-1, num_features))    distances = LA.norm(expanded_array1-expanded_array2, axis=1)    distances = np.reshape(distances, (num_point, num_point))    distances = np.min(distances, axis=1)    distances = np.mean(distances)    return distancesdef chamfer_distance_numpy(array1, array2):    batch_size, num_point, num_features = array1.shape    dist = 0    for i in range(batch_size):        av_dist1 = array2samples_distance(array1[i], array2[i])        av_dist2 = array2samples_distance(array2[i], array1[i])        dist = dist + (av_dist1+av_dist2)/batch_size    return dist

You can validate the result using following script:

batch_size = 8num_point = 20num_features = 4np.random.seed(1)array1 = np.random.randint(0, high=4, size=(batch_size, num_point, num_features))array2 = np.random.randint(0, high=4, size=(batch_size, num_point, num_features))print('sklearn: ', chamfer_distance_sklearn(array1, array2))print('numpy: ', chamfer_distance_numpy(array1, array2))array1_tf = tf.constant(array1, dtype=tf.float64)array2_tf = tf.constant(array2, dtype=tf.float64)dist_tf = chamfer_distance_tf(array1_tf, array2_tf)with tf.Session() as sess:    print('tf: ', sess.run(dist_tf))