cloudViewer.geometry.KDTreeFlann

class cloudViewer.geometry.KDTreeFlann

KDTree with FLANN for nearest neighbor search.

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: cloudViewer.geometry.KDTreeFlann) -> None

2. __init__(self: cloudViewer.geometry.KDTreeFlann, data: typing.Annotated[numpy.typing.ArrayLike, numpy.float64, “[m, n]”]) -> None

3. __init__(self: cloudViewer.geometry.KDTreeFlann, geometry: cloudViewer.geometry.ccHObject) -> None

4. __init__(self: cloudViewer.geometry.KDTreeFlann, feature: cloudViewer::utility::Feature) -> None

query_vector_3d(self: cloudViewer.geometry.KDTreeFlann, queries: cloudViewer.utility.Vector3dVector, search_param: cloudViewer.geometry.KDTreeSearchParam) → tuple[int, list[cloudViewer.utility.IntVector], list[cloudViewer.utility.DoubleVector]]

query_vector_xd(self: cloudViewer.geometry.KDTreeFlann, queries: collections.abc.Sequence[Annotated[numpy.typing.ArrayLike, numpy.float64, '[m, 1]']], search_param: cloudViewer.geometry.KDTreeSearchParam) → tuple[int, list[cloudViewer.utility.IntVector], list[cloudViewer.utility.DoubleVector]]

search_hybrid_vector_3d(self, query, radius, max_nn)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• radius (SupportsFloat) – Search radius.

• max_nn (SupportsInt) – At maximum, max_nn neighbors will be searched.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_hybrid_vector_xd(self, query, radius, max_nn)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• radius (SupportsFloat) – Search radius.

• max_nn (SupportsInt) – At maximum, max_nn neighbors will be searched.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_knn_vector_3d(self, query, knn)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• knn (SupportsInt) – knn neighbors will be searched.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_knn_vector_xd(self, query, knn)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• knn (SupportsInt) – knn neighbors will be searched.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_radius_vector_3d(self, query, radius)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• radius (SupportsFloat) – Search radius.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_radius_vector_xd(self, query, radius)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• radius (SupportsFloat) – Search radius.

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_vector_3d(self, query, search_param)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• search_param (cloudViewer.geometry.KDTreeSearchParam) –

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

search_vector_xd(self, query, search_param)

Parameters:

• query (Annotated[numpy.typing.ArrayLike, numpy.float64,) – The input query point.

• search_param (cloudViewer.geometry.KDTreeSearchParam) –

Returns:

tuple[int, cloudViewer.utility.IntVector, cloudViewer.utility.DoubleVector]

set_feature(self, feature)

Sets the data for the KDTree from the feature data.

Parameters:

feature (cloudViewer::utility::Feature) – Feature data.

Returns:

bool

set_geometry(self, geometry)

Sets the data for the KDTree from geometry.

Parameters:

geometry (cloudViewer.geometry.ccHObject) –

Returns:

bool

set_matrix_data(self, data)

Sets the data for the KDTree from a matrix.

Parameters:

data (Annotated[numpy.typing.ArrayLike, numpy.float64,) – Matrix data.

Returns:

bool