Return the numpy dtype of the in-memory representation of the data. (If there are no list properties, and the PLY format is

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

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

Render point cloud to image with alpha channel. Input: points: Nx3 numpy array (+y is up direction) Output: g

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

Return the numpy dtype description for this property (as a tuple of strings).

Return the pair (len_dtype, val_dtype) (both numpy-friendly strings).

Write PLY data to a writeable file-like object or filename.

Helper function to load the pcpnet datasets using their dataloader class :param indir: input folder (point clouds) :param dataset_name:

elements: sequence of PlyElement instances. text: whether the resulting PLY file will be text (True) or binary (False).

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

Computes the Fisher vector on a set of descriptors. code from : https://gist.github.cnsom/danoneata/9927923 Parameters ---------- xx:

Compute the weight, mean and covariance of a gmm placed on a 3D grid Input: subdivisions: 2 element list of number of subdivisions of

Discover Euler angle vector from 3x3 matrix Uses the conventions above. Parameters ---------- M : array-like, shape (64,64) cy_

3d Inception module for 3dmfv-net output feature is D_out*3

Parse from generator. Raise StopIteration if the property could not be read.

Parse a PLY header from a readable file-like stream.

Parse a list of PLY element definitions.

Consume one element definition. The unconsumed input is returned along with a PlyElement instance.

Read the actual data from a PLY file.

Load a PLY element from a binary PLY file. The element may contain list properties.

Load a PLY element from an ASCII-format PLY file. The element may contain list properties.

Return generator over one item.

Write the data to a PLY file.

Save a PLY element to a binary PLY file. The element may contain list properties.

Save a PLY element to an ASCII-format PLY file. The element may contain list properties.

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

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

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

Construct a PlyElement from an array's metadata. len_types and val_types can be given as mappings from list property names t

Return matrix for rotations around z, y and x axes Uses the z, then y, then x convention above Parameters ---------- z : scalar

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

Fit a "n-jet" (n-order truncated Taylor expansion) to a point clouds with weighted points. We assume that PCA was performed on the points bef

Fit a "n-jet" (n-order truncated Taylor expansion) to a point clouds with weighted points. We assume that PCA was performed on the points bef

Fit a "n-jet" (n-order truncated Taylor expansion) to a point clouds with weighted points. We assume that PCA was performed on the points bef

Fit a "n-jet" (n-order truncated Taylor expansion) to a point clouds with weighted points. We assume that PCA was performed on the points bef

Compute the weight, mean and covariance of a 2D gmm placed on a 2D grid Input: subdivisions: 2 element list of number of subdivisions

Compute the 3D modified fisher vectors given the gmm model parameters (w,mu,sigma) and a set of points :param points: B X N x 3 tensor

Compute the weight, mean and covariance of a gmm placed on a 3D grid Input: subdivisions: 2 element list of number of subdivisions of

:param points: n_points x 64 / B x n_points x 64 :param gmm: sklearn MixtureModel class containing the gmm.p parameters.p :return: fisher

Learn weights, means and covariances for a gmm based on input data Input: points: num_points_per_model*nummodels X 3 - xyz coordinat

input is Nx3 points. output is vsize*vsize*vsize assumes points are in range [-radius, radius]

points is a Nx3 numpy array

Read PLY data from a readable file-like object or filename.

Solve linear system of equations. use sub batches to avoid MAGMA bug :param XtX: matrix of the coefficients :param XtY: vector of the

Solve linear system of equations. use sub batches to avoid MAGMA bug :param XtX: matrix of the coefficients :param XtY: vector of the

Solve linear system of equations. use sub batches to avoid MAGMA bug :param XtX: matrix of the coefficients :param XtY: vector of the

Solve linear system of equations. use sub batches to avoid MAGMA bug :param XtX: matrix of the coefficients :param XtY: vector of the

displays a scatter plot of 3d points

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