:param radius: float, radius of ball :param nsample: int, maximum number of features to gather in the ball :param use_xyz:

:param radius: float, radius of ball :param nsample: int, maximum number of features to gather in the ball :param use_xyz:

:param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples i

:param mlp: list of int :param bn: whether to use batchnorm

:param ctx: :param grad_out: (B, C, N) tensor with gradients of outputs :return: grad_features: (B, C, M) tensor

:param ctx: :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward :return:

Loads a JSON camera into memory.

Estimate the bounding sphere given camera pose

Evaluate spherical harmonics at unit directions using hardcoded SH polynomials. Works with torch/np/jnp. ... Can be 0 or more bat

x: position of canonical gaussians [N, 3]

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

Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance :param ctx:

:param ctx: :param features: (B, C, N) :param idx: (B, npoint) index tensor of the features to gather :return:

Find the three nearest neighbors of unknown in known :param ctx: :param unknown: (B, N, 3) :param known: (B, M, 3)

Find the three nearest neighbors of unknown in known :param ctx: :param unknown: (B, N, 3) :param known: (B, M, 3)

Performs weight linear interpolation on 3 features :param ctx: :param features: (B, C, M) Features descriptors to be interpol

:param ctx: :param features: (B, C, N) tensor of features to group :param idx: (B, npoint, nsample) tensor containing the ind

:param ctx: :param radius: float, radius of the balls :param nsample: int, maximum number of features in the balls :p

:param xyz: (B, N, 3) xyz coordinates of the features :param new_xyz: (B, npoint, 3) centroids :param features: (B, C, N) des

:param xyz: (B, N, 3) xyz coordinates of the features :param new_xyz: ignored :param features: (B, C, N) descriptors of the f

:param xyz: (B, N, 3) xyz coordinates of the features :param new_xyz: (B, M, 3) centroids :param idx: (B, M, K) centroids

:param xyz: (B, N, 3) tensor of the xyz coordinates of the features :param features: (B, N, C) tensor of the descriptors of the the f

:param unknown: (B, n, 3) tensor of the xyz positions of the unknown features :param known: (B, m, 3) tensor of the xyz positions of

q is the output from the network (rotation from t=0.5 to t=1) gt_R is the GT rotation from t=0 to t=1