(
    in_signal, pc_dim_out, non_linearity=tf.nn.relu
)
return out_signal
A sampling network that generate sample points from an input point cloud.
A sampling network that generate sample points from an input point cloud.
A sampling network that generate sample points from an input point cloud .
def sampler_with_convs_and_symmetry_and_fc(
    in_signal, pc_dim_out, non_linearity=tf.nn.relu
):
    """A sampling network that generate sample points from an input point cloud."""

    n_points, dummy = pc_dim_out
    if dummy != 3:
        raise ValueError()

    encoder_args = {
        "n_filters": [64, 128, 128, 256, 128],
        "filter_sizes": [1],
        "strides": [1],
        "non_linearity": non_linearity,
        "b_norm": True,
        "verbose": True,
    }
    layer = encoder_with_convs_and_symmetry(in_signal, **encoder_args)

    decoder_args = {
        "layer_sizes": [256, 256, np.prod([n_points, 3])],
        "b_norm": False,
        "b_norm_finish": False,
        "verbose": True,
    }
    out_signal = decoder_with_fc_only(layer, **decoder_args)

    out_signal = tf.reshape(out_signal, [-1, n_points, 3])
    return out_signal
def sampler_with_convs_and_symmetry_and_fc ( in_signal , pc_dim_out , non_linearity = tf . nn . relu ) : n_points , dummy = pc_dim_out if dummy != 3 : raise ValueError ( ) encoder_args = { "n_filters" : [ 64 , 128 , 128 , 256 , 128 ] , "filter_sizes" : [ 1 ] , "strides" : [ 1 ] , "non_linearity" : non_linearity , "b_norm" : True , "verbose" : True , } layer = encoder_with_convs_and_symmetry ( in_signal , * * encoder_args ) decoder_args = { "layer_sizes" : [ 256 , 256 , np . prod ( [ n_points , 3 ] ) ] , "b_norm" : False , "b_norm_finish" : False , "verbose" : True , } out_signal = decoder_with_fc_only ( layer , * * decoder_args ) out_signal = tf . reshape ( out_signal , [ - 1 , n_points , 3 ] ) return out_signal