pointnet tf_util.fully_connected

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)

看下具體實現，bn代表batch norm，batch方向的歸一化處理

def fully_connected(inputs,
                    num_outputs,
                    scope,
                    use_xavier=True,
                    stddev=1e-3,
                    weight_decay=0.0,
                    activation_fn=tf.nn.relu,
                    bn=False,
                    bn_decay=None,
                    is_training=None):
  """ Fully connected layer with non-linear operation.
  
  Args:
    inputs: 2-D tensor BxN
    num_outputs: int
  
  Returns:
    Variable tensor of size B x num_outputs.
  """
  with tf.variable_scope(scope) as sc:
    num_input_units = inputs.get_shape()[-1].value
    weights = _variable_with_weight_decay('weights',
                                          shape=[num_input_units, num_outputs],
                                          use_xavier=use_xavier,
                                          stddev=stddev,
                                          wd=weight_decay)
    outputs = tf.matmul(inputs, weights)
    biases = _variable_on_cpu('biases', [num_outputs],
                             tf.constant_initializer(0.0))
    outputs = tf.nn.bias_add(outputs, biases)
     
    if bn:
      outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn')

    if activation_fn is not None:
      outputs = activation_fn(outputs)
    return outputs

num_input_units=1024,輸出爲512在pointnet分類工程中，則聲明一個1024，512的weight的tensor一個biases，512，進行歸一化

最後設置激活函數，依然是relu

所以，fullyconnet實際是一個正向x*w+b的一個操作，只是通過控制w來控制輸出。沒有卷積的動作