tfg.geometry.representation.mesh.sampler.area_weighted_random_sample_triangle_mesh

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

tfg.geometry.representation.mesh.sampler.area_weighted_random_sample_triangle_mesh(
    vertex_attributes: type_alias.TensorLike,
    faces: type_alias.TensorLike,
    num_samples: int,
    vertex_positions: Optional[type_alias.TensorLike] = None,
    seed: Optional[type_alias.TensorLike] = None,
    stateless: bool = False,
    name: str = &#x27;area_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 a feature defined on each vertex. If `vertex_positions` is not provided, then first 3 dimensions of `vertex_attributes` denote the vertex positions.
`faces`	A `int` tensor of shape `[A1, ..., An, F, 3]`, where F is the number of faces.
`num_samples`	An `int` scalar denoting number of samples to be drawn from each mesh.
`vertex_positions`	An optional `float` tensor of shape `[A1, ..., An, V, 3]`, where V is the number of vertices. If None, then vertex_attributes[..., :3] is used as vertex positions.
`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 "area_weighted_random_sample_triangle_mesh".

Returns
`sample_pts`	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.area_weighted_random_sample_triangle_mesh

Note

Args

Returns