parameters: (self, batch_data, batch_gt=None, scale_low=0.5, scale_high=2)
return_statement: return batch_data, batch_gt, tf.squeeze(scales)
docstring: Randomly scale the point cloud. Scale is per point cloud.
              Input:
                  BxNx3 array, original batch of point clouds
              Return:
                  BxNx3 array, scaled batch of point clouds
docstring_summary: Randomly scale the point cloud. Scale is per point cloud.
              Input:
                  BxNx3 array, original batch of point clouds
              Return:
                  BxNx3 array, scaled batch of point clouds
docstring_tokens: Randomly scale the point cloud . Scale is per point cloud . Input : BxNx3 array original batch of point clouds Return : BxNx3 array scaled batch of point clouds
function: def random_scale_point_cloud_and_gt(self, batch_data, batch_gt=None, scale_low=0.5, scale_high=2):
          """ Randomly scale the point cloud. Scale is per point cloud.
              Input:
                  BxNx3 array, original batch of point clouds
              Return:
                  BxNx3 array, scaled batch of point clouds
          """
          B, N, C = batch_data.get_shape().as_list()
          scales = tf.random_uniform((B, 1, 1), minval=scale_low, maxval=scale_high, dtype=tf.float32)
  
          batch_data = tf.concat([batch_data[:, :, :3] * scales, batch_data[:, :, 3:]], axis=-1)
  
          if batch_gt is not None:
              batch_gt = tf.concat([batch_gt[:, :, :3] * scales, batch_gt[:, :, 3:]], axis=-1)
  
          return batch_data, batch_gt, tf.squeeze(scales)
function_tokens: def random_scale_point_cloud_and_gt ( self , batch_data , batch_gt = None , scale_low = 0.5 , scale_high = 2 ) : B , N , C = batch_data . get_shape ( ) . as_list ( ) scales = tf . random_uniform ( ( B , 1 , 1 ) , minval = scale_low , maxval = scale_high , dtype = tf . float32 ) batch_data = tf . concat ( [ batch_data [ : , : , : 3 ] * scales , batch_data [ : , : , 3 : ] ] , axis = - 1 ) if batch_gt is not None : batch_gt = tf . concat ( [ batch_gt [ : , : , : 3 ] * scales , batch_gt [ : , : , 3 : ] ] , axis = - 1 ) return batch_data , batch_gt , tf . squeeze ( scales )