Google I/O is a wrap! Catch up on TensorFlow sessions View sessions

tfg.geometry.representation.ray.sample_inverse_transform_stratified_1d

Sample points on a ray using inverse transform stratified sampling.

tfg.geometry.representation.ray.sample_inverse_transform_stratified_1d(
    ray_org: TensorLike,
    ray_dir: TensorLike,
    z_values_init: TensorLike,
    weights_init: TensorLike,
    n_samples: int,
    combine_z_values=True,
    name: str = 'sample_inverse_transform_stratified_1d'
)

The rays are defined by their origin and direction. Along each ray, there are M samples (provided as 1D distances from the ray origin) and the corresponding weights (probabilities) that facilitate the inverse transform sampling.

ray_org A tensor of shape [A1, ..., An, 3], where the last dimension represents the 3D position of the ray origin.
ray_dir A tensor of shape [A1, ..., An, 3], where the last dimension represents the 3D direction of the ray.
z_values_init A tensor of shape [A1, ..., An, M], where the last dimension is the location of M points along the ray.
weights_init A tensor of shape [A1, ..., An, M], where the last dimension is the density of M points along the ray.
n_samples A number M to sample on the ray.
combine_z_values Wether to combine the new 1D samples with the initial points.
name A name for this op that defaults to "stratified_sampling".

A tensor of shape [A1, ..., An, M, 3] indicating the M points on the ray and a tensor of shape [A1, ..., An, M] for the Z values on the points.