In this package, all functions optional parameters are implemented as BGL optional named parameters (see Named Parameters for more information on how to use them).

Since the parameters of the various point set processing functions defined in this package are redundant, their long descriptions are centralized below.

In the following, we assume that the following types are provided as template parameters of point set processing functions and classes. Note that, for some of these functions, the type is more specific.

PointRange is a model of Range. The value type of its iterator is the key type of the related property maps (see below).
GeomTraits a geometric traits class in which constructions are performed and predicates evaluated. Everywhere in this package, a CGAL Kernel fulfills the requirements.

CGAL::parameters::all_default() can be used to indicate that default values of optional named parameters must be used.

Here is the list of the named parameters available in this package:

geom_traits	the geometric traits instance in which the point set processing operation should be performed. Type: a Geometric traits class. Default type: is typename CGAL::Kernel_traits<point_map::value_type>::Kernel
point_map	is the property map containing the points associated to the iterators of the point range `points`. Type: a class model of `ReadablePropertyMap` with `PointRange::iterator` as key type and `geom_traits::Point_3` as value type. Default value: CGAL::Identity_property_map<geom_traits::Point_3>
normal_map	is the property map containing the normal vectors associated to the iterators of the point range `points`. Type: a class model of `ReadablePropertyMap` with `PointRange::iterator` as key type and `geom_traits::Vector_3` as value type. No default value.
diagonalize_traits	is the solver used for diagonalizing covariance matrices. Type: a class model of `DiagonalizeTraits`. Default: `CGAL::Eigen_diagonalize_traits` if Eigen 3.2 (or greater) is available and `CGAL_EIGEN3_ENABLED` is defined.
svd_traits	is the linear algebra algorithm required by the jet fitting method. For more information, please refer to the documentation of `CGAL::Monge_via_jet_fitting`. Type: a class fitting the requirements of `CGAL::Monge_via_jet_fitting`. Default: if Eigen 3.2 (or greater) is available and `CGAL_EIGEN3_ENABLED` is defined, then `CGAL::Eigen_svd` is used.
callback	is a mechanism to get feedback on the advancement of the algorithm while it's running and to interrupt it if needed. It is called regularly when the algorithm is running: the current advancement (between 0. and 1.) is passed as parameter. If it returns `true`, then the algorithm continues its execution normally; if it returns `false`, the algorithm is stopped. The callback will be copied and therefore needs to be lightweight. Note that when a callback is run on a parallelized algorithm with `CGAL::Parallel_tag`, it is called asynchronously on a separate thread and shouldn't access or modify the variables that are parameters of the algorithm. Type: `CGAL::cpp11::function<bool(double)>`. Default: empty function.
query_point_map	is the property map containing the points associated to the iterators of the point range `queries`. Type: a class model of `ReadablePropertyMap` with `PointRange::iterator` as key type and `geom_traits::Point_3` as value type. Default value: CGAL::Identity_property_map<geom_traits::Point_3>
sharpness_angle	controls the sharpness of the result. The larger the value is, the smoother the result will be. The range of possible value is [0, 90]. Type: floating scalar value Default value: `30.`
edge_sensitivity	controls the priority of points inserted along sharp features. Larger values of edge-sensitivity give higher priority to inserting points along sharp features. The range of possible values is `[0, 1]`. Type: floating scalar value Default value: `1`.
neighbor_radius	is the spherical neighborhood radius. Type: floating scalar value Default value: `-1`, in which case the functions that use it will compute an adapted value automatically.
number_of_output_points	is the number of output points to generate. Type: `unsigned` `int` Default value: `1000`
size	maximum cluster size for hierarchical clustering. Must be strictly greather than 0. Type: `unsigned` `int` Default value: `10`
maximum_variation	maximum cluster size for hierarchical clustering. Must be strictly greather than 0. Type: `unsigned` `int` Default value: `10`
degree_fitting	is the degree of fitting (see `CGAL::Monge_via_jet_fitting`). Type: `unsigned` `int` Default value: `2`
degree_monge	is the Monge degree (see `CGAL::Monge_via_jet_fitting`). Type: `unsigned` `int` Default value: `2`
threshold_distance	is the minimum distance for a point to be considered as outlier (distance here is the square root of the average squared distance to K nearest neighbors). Type: floating scalar value Default value: `0`
threshold_percent	is the maximum percentage (between 0 and 100) of points to remove in `remove_outliers()`. Type: floating scalar value Default value: `10`
attraction_factor	multiple of a tolerance `epsilon` used to connect simplices. Type: floating scalar value Default value: `3`
plane_map	is the property map containing the planes associated to the iterators of the plane range `planes`. Type: a class model of `ReadablePropertyMap` with `PlaneRange::iterator` as key type and `geom_traits::Plane_3` as value type. Default value: CGAL::Identity_property_map<geom_traits::Plane_3>
plane_index_map	is the property map that associates the index of a point in the input range to the index of plane (-1 if point is not assigned to a plane). Type: a class model of `ReadablePropertyMap` with `std::size_t` as key type and `int` as value type. No default value.
select_percentage	percentage of points to retain (between 0 and 100) in `wlop_simplify_and_regularize_point_set()`. Type: floating scalar value Default value: 5.
number_of_iterations	number of iterations to solve the optimsation problem in `wlop_simplify_and_regularize_point_set()`. Type: `unsigned` `int` Default value: 35.
require_uniform_sampling	turn on or off an optional preprocessing of `wlop_simplify_and_regularize_point_set()`, which will give better result if the distribution of the input points is highly non-uniform. Type: `bool` Default value: `false`