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]

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

Args
`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".

Returns
`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]`.

tfg.geometry.representation.mesh.sampler.weighted_random_sample_triangle_mesh

Note

Args

Returns