tfg.geometry.representation.mesh.sampler.weighted_random_sample_triangle_mesh

Performs a face probability weighted random sampling of a tri mesh.

tfg.geometry.representation.mesh.sampler.weighted_random_sample_triangle_mesh(
    vertex_attributes: type_alias.TensorLike,
    faces: type_alias.TensorLike,
    num_samples: int,
    face_weights: type_alias.TensorLike,
    seed: Optional[type_alias.TensorLike] = None,
    stateless: bool = False,
    name: str = 'weighted_random_sample_triangle_mesh'
) -> Tuple[type_alias.TensorLike, type_alias.TensorLike]

In the following, A1 to An are optional batch dimensions.

vertex_attributes A float tensor of shape [A1, ..., An, V, D], where V is the number of vertices, and D is dimensionality of each vertex.
faces A int tensor of shape [A1, ..., An, F, 3], where F is the number of faces.
num_samples A int 0-D tensor denoting number of samples to be drawn from each mesh.
face_weights A float tensor of shape `[A1, ..., An, F], denoting unnormalized sampling probability of each face, where F is the number of faces.
seed Optional random seed.
stateless Optional flag to use stateless random sampler. If stateless=True, then seed must be provided as shape [2] int tensor. Stateless random sampling is useful for testing to generate same sequence across calls.
name Name for op. Defaults to "weighted_random_sample_triangle_mesh".

sample_points A float tensor of shape [A1, ..., An, num_samples, D], where D is dimensionality of each sampled point.
sample_face_indices A int tensor of shape [A1, ..., An, num_samples].