PointNet代码分析

train.py (训练相关)

train_one_epoch函数

• shuffle函数打乱输入数据的顺序
• 使用provider.py文件中的jitter函数和rotate函数对数据作随机处理

eval_one_epoch

• 计算acc和loss

provider.py (获取数据集，旋转，扰动函数)

jitter_point_cloud函数

def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05):
    B, N, C = batch_data.shape
    assert(clip > 0)
    jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip)
    jittered_data += batch_data
    return jittered_data

• 利用randn产生三个维度上的符合高斯分布的随机数
• 利用clip函数将三个维度的扰动限制在(sigma,-sigma)

rotate_point_cloud_by_angle函数

def rotate_point_cloud_by_angle(batch_data, rotation_angle):
    rotated_data = np.zeros(batch_data.shape, dtype=np.float32)
    for k in range(batch_data.shape[0]):
        #rotation_angle = np.random.uniform() * 2 * np.pi
        cosval = np.cos(rotation_angle)
        sinval = np.sin(rotation_angle)
        rotation_matrix = np.array([[cosval, 0, sinval],
                                    [0, 1, 0],
                                    [-sinval, 0, cosval]])
        shape_pc = batch_data[k, ...]
        rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix)
    return rotated_data

• 原向量乘上转换矩阵，得到旋转后的向量

pointnet_cls.py（分类网络模型）

get_model函数

Pointnet.png

with tf.variable_scope('transform_net1') as sc:
    transform = input_transform_net(point_cloud, is_training, bn_decay, K=3)
point_cloud_transformed = tf.matmul(point_cloud, transform)
input_image = tf.expand_dims(point_cloud_transformed, -1)

• T-net处理输入获得转换矩阵，矩阵相乘对齐输入

net = tf_util.conv2d(input_image, 64, [1,3],
                     padding='VALID', stride=[1,1],
                     bn=True, is_training=is_training,
                     scope='conv1', bn_decay=bn_decay)
   
net = tf_util.conv2d(net, 64, [1,1],
                     padding='VALID', stride=[1,1],
                     bn=True, is_training=is_training,
                     scope='conv2', bn_decay=bn_decay)

• 两层卷积，即图中mlp(64,64)

with tf.variable_scope('transform_net2') as sc:
    transform = feature_transform_net(net, is_training, bn_decay, K=64)
end_points['transform'] = transform
net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform)
net_transformed = tf.expand_dims(net_transformed, [2])

• T-net处理特征得到转换矩阵，矩阵相乘对齐特征

net = tf_util.conv2d(net_transformed, 64, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv3', bn_decay=bn_decay)
net = tf_util.conv2d(net, 128, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv4', bn_decay=bn_decay)
net = tf_util.conv2d(net, 1024, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='conv5', bn_decay=bn_decay)

• 三层卷积继续提取特征，即mlp(64,128,1024)

net = tf_util.max_pool2d(net, [num_point,1],
                         padding='VALID', scope='maxpool')

• 池化层作为对称函数，得到1024为的特征向量，解决点云的无序性

net = tf.reshape(net, [batch_size, -1])
net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
                              scope='fc1', bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training,
                      scope='dp1')
net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
                              scope='fc2', bn_decay=bn_decay)
net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training,
                      scope='dp2')
net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3')

• mlp(512,256,k)，这时k取40

get_loss函数

• 利用交叉熵计算loss

transform_net.py

def input_transform_net(point_cloud, is_training, bn_decay=None, K=3):
    """ Input (XYZ) Transform Net, input is BxNx3 gray image
        Return:
            Transformation matrix of size 3xK """
    batch_size = point_cloud.get_shape()[0].value
    num_point = point_cloud.get_shape()[1].value

    input_image = tf.expand_dims(point_cloud, -1)
    net = tf_util.conv2d(input_image, 64, [1,3],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv1', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 128, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv2', bn_decay=bn_decay)
    net = tf_util.conv2d(net, 1024, [1,1],
                         padding='VALID', stride=[1,1],
                         bn=True, is_training=is_training,
                         scope='tconv3', bn_decay=bn_decay)
    net = tf_util.max_pool2d(net, [num_point,1],
                             padding='VALID', scope='tmaxpool')

    net = tf.reshape(net, [batch_size, -1])
    net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training,
                                  scope='tfc1', bn_decay=bn_decay)
    net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training,
                                  scope='tfc2', bn_decay=bn_decay)

    with tf.variable_scope('transform_XYZ') as sc:
        assert(K==3)
        weights = tf.get_variable('weights', [256, 3*K],
                                  initializer=tf.constant_initializer(0.0),
                                  dtype=tf.float32)
        biases = tf.get_variable('biases', [3*K],
                                 initializer=tf.constant_initializer(0.0),
                                 dtype=tf.float32)
        biases += tf.constant([1,0,0,0,1,0,0,0,1], dtype=tf.float32)
        transform = tf.matmul(net, weights)
        transform = tf.nn.bias_add(transform, biases)

    transform = tf.reshape(transform, [batch_size, 3, K])
    return transform

• 针对输入的T-net，输出一个转移矩阵用于和输入相乘