Convert angle, axis pair to Euler angles Parameters ---------- theta : scalar angle of rotation vector : 3 element sequence

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

Return angle, axis corresponding to these Euler angles Uses the z, then y, then x convention above Parameters ---------- z : scalar

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

pred: BxNxC, label: BxN, smpw: BxN

pred: BxNxC, label: BxN, smpw: BxN

K: neighbor size src_xyz: original point xyz (batch_size, ndataset, 3) q_xyz: query point xyz (batch_size, npoint, 3)

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 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

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

Return Euler angles corresponding to quaternion `q` Parameters ---------- q : 4 element sequence w, x, y, z of quaternion Re

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

read XYZRGBA 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 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:

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

inputs: npoint: scalar, number of points to sample pointcloud: B * N * 3, input point cloud output: sub_pts: B * npoint * 3, sub-

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.

Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file

Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file

Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file

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