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

Making the bouning box information Finding the Minimum and Maximum points of X, Y and Z and add the epsilon value Arg

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

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

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).

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

ConvNet baseline, input is BxNx3 gray image

Making the wall information using PointCloud data Clustering the original point cloud data. Extracting the plane data from th

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

Check if the bounding box intersects Check if the bounding box intersects using the min and max points of the bounding box. Args

Checking the distance between point and plane Find the number of points where the distance between the planes is 0.05 or less and cal

Clustering the orginal point cloud data The voxelization of the original data for the downsampling. Extract clustered data fr

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

Finding the plane from point cloud data Calculate the surface normals for each point by fitting a plane to the nearest. Args:

Calculate the size of the area Calculate the size of the area with data using a bounding box. Args: bbox_range: Boun

pred: B,N,13 label: B,N

ConvNet baseline, input is BxNx3 gray image

ConvNet baseline, input is BxNx3 gray image

ConvNet baseline, input is BxNx3 gray image

ConvNet baseline, input is BxNx3 gray image

ConvNet baseline, input is BxNx3 gray image

ConvNet baseline, input is BxNx3 gray image

Generate edge information to create a graph Using the intersection of each face, create edge information that makes up the face

Find and subtract the minimum value of the segmented data If the number of points of each data in the list is 500 or more, the minimum value

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

Prepare block training data. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is s

Visualizing Pointcloud data Args: cloud_list: list of PointClouds Returns: Visualizing Pointclouds data

1. 26 - building object 2. 41 - room 3. 31 - interior wallsurface 4. 32 - floor 5. 30 - ceilling

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 train

Check if the bounding box intersects Check if the bounding box intersects using the min and max points of the bounding box. Args

Args: s_point: Start point of line segment e_point: End point of line segment point: [x, y, z] point to check e:

Check whether the pointer is included in the bounding box Check the pointer's X, Y and Z values are included in the bounding box

Clustering the orginal point cloud data The voxelization of the original data for the downsampling. Extract clustered data fr

Finding the plane from point cloud data Calculate the surface normals for each point by fitting a plane to the nearest. Args:

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

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