Input: points: (batch_size, ndataset, channel) float32 array, points to sample from idx: (batch_size, npoint, nsample) int32 arra

Helper to create a Variable stored on CPU memory. Args: name: name of the variable shape: list of ints initializer: initializer for Vari

Helper to create an initialized Variable with weight decay. Note that the Variable is initialized with a truncated normal distribution. A weight

Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-te

PointNet Feature Propogation (FP) Module Input: xyz1: (batch_size, ndataset1, 3) TF tensor xyz2: (batch_size, ndatase

PointNet Set Abstraction (SA) Module Input: xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, c

Input: radius: float32, ball search radius nsample: int32, number of points selected in each ball region xyz1: (batch_siz

Input: points: (b,m,c) float32 array, known points idx: (b,n,3) int32 array, indices to known points weight: (b,n,3) floa

Input: xyz1: (b,n,3) float32 array, unknown points xyz2: (b,m,3) float32 array, known points Output: dist: (b,n,3) fl

Batch normalization on 2D convolutional maps. Args: inputs: Tensor, 4D BHWC input maps is_training: boolean tf.Varialbe, true i

input: int32 batch_size * ndataset * 3 float32 returns: batch_size * npoint int32

input: batch_size * ndataset * 3 float32 batch_size * npoints int32 returns: batch_size * npoints * 3 float32

Batch normalization on 1D convolutional maps. Args: inputs: Tensor, 3D BLC input maps is_training: boolean tf.Varialbe, true in

Batch normalization on 3D convolutional maps. Args: inputs: Tensor, 5D BDHWC input maps is_training: boolean tf.Varialbe, true

Batch normalization on FC data. Args: inputs: Tensor, 2D BxC input is_training: boolean tf.Varialbe, true indicates training ph

CONSTANT WEIGHT TODO input: dist (b,n,3) output: weight (b,n,3)

input: points (b,n,c), idx (b,m,nsample) output: out (b,m,nsample,c)

input: grad_out (b,m,nsample,c), idx (b,m,nsample), output: grad_points (b,n,c)

input: points (b,m,c), idx (b,n,3), weight (b,n,3) output: out (b,n,c)

input: grad_out (b,n,c), idx (b,n,3), weight (b,n,3) output: grad_points (b,m,c)

Input: k: int32, number of k in k-nn search xyz1: (batch_size, ndataset, c) float32 array, input points xyz2: (batch_size

:param xyz: (b, n, 3) float :param radius: float :return: (b, n, 8) int

:param xyz: (b, n, 3) float :param radius: float :return: idx: (b, n, 32) int

input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) output: idx (b,m,nsample)

Input: npoint: int32 radius: float32 nsample: int32 xyz: (batch_size, ndataset, 3) TF tensor points: (bat

Inputs: xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as

Input: k: int32, number of k SMALLEST elements selected dist: (b,m,n) float32 array, distance matrix, m query points, n dataset p

input: k (1), distance matrix dist (b,m,n) output: idx (b,m,n), val (b,m,n)

input: points (b,m,c), idx (b,n,3), weight (b,n,3) output: out (b,n,c)

input: grad_out (b,n,c), idx (b,n,3), weight (b,n,3) output: grad_points (b,m,c)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

2D avg pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable ten

3D avg pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable t

NOTE: this is older version of the util func. it is deprecated. Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflo

1D convolution with non-linear operation. Args: inputs: 3-D tensor variable BxLxC num_output_channels: int kernel_size: int scope:

2D convolution with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2

2D convolution transpose with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a