Read data from a binary stream. Raise StopIteration if the property could not be read.

Return generator over the (numerical) PLY representation of the list data (length followed by actual data).

Write data to a binary stream.

Write data to a binary stream.

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

Checking the outline point is near by main plane Args: normal_vector: Equation of main plane point_list: Outline point(4) of sub

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

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

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filename pr

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filename pr

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

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 Args:

Checking the point included in certain area Args: points: [x, y, z] point bbox: Bounding box information [[minX, minY, minZ], [ma

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

Convert a cmp= function into a key= function

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Convert original dataset files to data_label file (each line is XYZRGBL). We aggregated all the points from each instance in the room. A

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

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

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

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

Calculate the distance between two points Args: point1: information of point (x, y, z) point2: information of point (

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

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

List properties always have a numpy dtype of "object".

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

:param model_path: trained model path :param filepath: file stored dir :param booth_data: point cloud data type : Array / value [x, y, z

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

Make Cos value between 2 planes Args: coeff_1: Equation of plane coeff_2: Equation of plane Returns: model_cos: cos

Create intersection points between planes Search intersecting lines between planes where each bounding box intersects. Create

Making the ration of line segment Args: p_cloud: [x, y, z] point list normal_vector: Equation of sorted_PointList:

Planar data extraction from clustered cloud Execute the loop statement until the cloud size is greater than or equal to the clustered

PointCloud Voxelization Downsampling using voxelization Args: points: [x, y, z] data list colors: [r, g, b] data list

Making the GridMap information Dividing the PointCloud data using grid_size Args: points: [x, y, z, r, g, b] data list gri

Provide PLY-formatted metadata for the instance.

Format this element's metadata as it would appear in a PLY header.

Make a 2D array from an array of arrays. The `cols' and `dtype' arguments can be omitted if the array is not empty.

Making the chair information Clustering the chair data. And returns the bounding box of each clustered data Args:

Merging the data as file Args: data_list: PointCloud list Returns: Save merged PointCloud data

Making the Side Line using bounding box and plane equation Finding the intersection point between the equation of the plane and the bounding

Making the straight information and finding the intersect point Generate straight line data using point_1 and point_2. Findin

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

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

Merging the parallel planes If the planes are parallel and the bounding boxes overlap, they are merged into one plane. Args:

Merging the data Args: data_list: PointCloud list Returns: merged_cloud: Merged PointCloud data bbox_info: Bounding

Clustering the exported plane data Finding clustered points in the data extracted as planes. Args: cloud_data: Point

Reading the .ply data and divide space Dividing the entire PointCloud data into a certain size. Find the minimum value of each segmented data

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

For visualization of a room from data_label file, input_filename: each line is X Y Z R G B L out_filename: OBJ filename, visualize inpu

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

Creating the WKT information using room info Making the SOLID information usin each room information Args: roomIndex: Clustered a