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

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

Randomly rotate the point clouds to augument the dataset rotation is per shape based along three axis Input: BxNx3 array, origina

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

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

r""" Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns

r""" Parameters ---------- grad_out : torch.Tensor (B, C, npoint, nsample) tensor of the gradients of the output

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

Input: xyz: input points position data, [B, C, N] points: input points data, [B, D, N] Return: ne

Input: xyz: input points position data, [B, C, N] points: input points data, [B, D, N] Return: ne

Input: xyz1: input points position data, [B, C, N] xyz2: sampled input points position data, [B, C, S] po

r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Paramet

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, n

r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor

r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor

r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of features to group idx : torch.Tens

r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Te

Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return: BxNx3

Normalize the batch data, use coordinates of the block centered at origin, Input: BxNxC array Output: BxNxC a

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

batch_pc: BxNx3

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

Randomly perturb the point clouds by small rotations Input: BxNx3 array, original batch of point clouds Return: B

Randomly perturb the point clouds by small rotations Input: BxNx6 array, original batch of point clouds and point normals R

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:

Rotate the point cloud along up direction with certain angle. Input: BxNx6 array, original batch of point clouds with normal

Randomly rotate XYZ, normal point cloud. Input: batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal

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: