reset order of h5 files

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

Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled dat

ops: dict mapping from string to tf ops

ops: dict mapping from string to tf ops

ops: dict mapping from string to tf ops

ops: dict mapping from string to tf ops

ops: dict mapping from string to tf ops

vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array.

vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array.

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

3D convolution with non-linear operation. Args: inputs: 5-D tensor variable BxDxHxWxC num_output_channels: int kernel_size: a list of

Dropout layer. Args: inputs: tensor is_training: boolean tf.Variable scope: string keep_prob: float in [0,1] noise_shape: list

Fully connected layer with non-linear operation. Args: inputs: 2-D tensor BxN num_outputs: int Returns: Variable tensor of size

pred: B*NUM_CLASSES, label: B,

pred: BxNxC, label: BxN,

pred: B*NUM_CLASSES, label: B,

pred: BxNxC, label: BxN, smpw: BxN

pred: B*NUM_CLASSES, label: B,

pred: BxNxC, label: BxN,

Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return: BxNx3

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

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

Input is BxNx3 a batch of point cloud Output is BxIxIxnum_samplex3 Added on Feb 19

Input is BxNx3 a batch of point cloud Output is BxIxIxnum_samplex3 Added on Feb 19

Input is BxNx3 batch of point cloud Output is Bx(vsize^3)

Input is BxNx3 batch of point cloud Output is Bx(vsize^3)

Input is BxNx3 a batch of point cloud Output is BxVxVxVxnum_samplex3 Added on Feb 19

Input is BxNx3 a batch of point cloud Output is BxVxVxVxnum_samplex3 Added on Feb 19

vol is of size vsize*vsize*vsize output an image to output_filename

vol is of size vsize*vsize*vsize output an image to output_filename

batch_pc: BxNx3

Randomly scale the point cloud. Scale is per point cloud. Input: BxNx3 array, original batch of point clouds Return:

read XYZ point cloud from filename PLY file

read XYZRGB point cloud from filename PLY file

Randomly perturb the point clouds by small rotations Input: BxNx3 array, original batch of point clouds Return: B

Randomly perturb the point clouds by small rotations Input: BxNx6 array, original batch of point clouds and point normals R

Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array,

Rotate the point cloud along up direction with certain angle. Input: BxNx3 array, original batch of point clouds Return:

Rotate the point cloud along up direction with certain angle. Input: BxNx6 array, original batch of point clouds with normal

Randomly rotate XYZ, normal point cloud. Input: batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal

Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array,

Randomly shift point cloud. Shift is per point cloud. Input: BxNx3 array, original batch of point clouds Return:

Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled dat

Shuffle orders of points in each point cloud -- changes FPS behavior. Use the same shuffling idx for the entire batch. Input:

input: Nx3, write points to filename as PLY format.