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

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

Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data,

Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data,

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

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

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

Input: xyz: pointcloud data, [B, N, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npo

Input: xyz: pointcloud data, [B, N, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npo

Input: xyz: pointcloud data, [N, D] npoint: number of samples Return: centroids: sampled pointcloud index, [npoint, D

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

PARAMETERS

PARAMETERS

Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz:

Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz:

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

Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn

Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn

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.

Construct a PlyElement from an array's metadata. len_types and val_types can be given as mappings from list property names to

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

input points Nx3 numpy array (+y is up direction). return an numpy array gray image of size 500x1500.

Demo for draw_point_cloud function

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.

room2block with input filename and RGB preprocessing.

room2block, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

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

room2sample, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

Input: npoint: radius: nsample: xyz: input points position data, [B, N, 3] points: input points data, [B,

Input: npoint: radius: nsample: xyz: input points position data, [B, N, 3] points: input points data, [B,

Input: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled point

Input: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled point

Input: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled point

Input: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled point

data is in N x ... we want to keep num_samplexC of them. if N > num_sample, we will randomly keep num_sample of them. if N <

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