Here is the list of all concepts and classes of this package. Classes are inside the namespace CGAL. Concepts are in the global namespace.

[detail level 12]

▼NCGAL
CDistance_adapter	A class that uses a point property map to adapt a distance class to work on a key as point type
CEuclidean_distance	The class `Euclidean_distance` provides an implementation of the concept `OrthogonalDistance`, with the Euclidean distance ( \( l_2\) metric)
CEuclidean_distance_sphere_point	The class `Euclidean_distance_sphere_point` provides an implementation of the `GeneralDistance` concept for the Euclidean distance ( \( l_2\) metric) between a \( d\)-dimensional sphere and a point, and the Euclidean distance between a \( d\)-dimensional sphere and a \( d\)-dimensional iso-rectangle defined as a \(k\)- \(d\) tree rectangle
CFair	Implements the fair splitting rule
CFuzzy_iso_box	The class `Fuzzy_iso_box` implements fuzzy `d`-dimensional (closed) iso boxes
CFuzzy_sphere	The class `Fuzzy_sphere` implements fuzzy `d`-dimensional spheres
CIncremental_neighbor_search	The class `Incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree
CK_neighbor_search	The class `K_neighbor_search` implements approximate `k`-nearest and `k`-furthest neighbor searching using standard search on a tree using a general distance class
CKd_tree	The class `Kd_tree` defines a `k-d` tree
CKd_tree_internal_node
CKd_tree_leaf_node
CKd_tree_node	The class `Kd_tree_node` implements a node class for a `k-d` tree
CKd_tree_rectangle	The class `Kd_tree_rectangle` implements `d`-dimensional iso-rectangles and related operations, e.g., methods to compute bounding boxes of point sets
CManhattan_distance_iso_box_point	The class `Manhattan_distance_iso_box_point` provides an implementation of the `GeneralDistance` concept for the Manhattan distance ( \( l_1\) metric) between a `d`-dimensional iso-box and a `d`-dimensional point and the Manhattan distance between a `d`-dimensional iso-box and a `d`-dimensional iso-box defined as a `k-d` tree rectangle
CMedian_of_max_spread	Implements the median of max spread splitting rule
CMedian_of_rectangle	Implements the median of rectangle splitting rule
CMidpoint_of_max_spread	Implements the midpoint of max spread splitting rule
CMidpoint_of_rectangle	Implements the midpoint of rectangle splitting rule
COrthogonal_incremental_neighbor_search	The class `Orthogonal_incremental_neighbor_search` implements incremental nearest and furthest neighbor searching on a tree
COrthogonal_k_neighbor_search	The class `Orthogonal_k_neighbor_search` implements approximate`k`-nearest and `k`-furthest neighbor searching on a tree using an orthogonal distance class
CPlane_separator	The class `Plane_separator` implements a plane separator, i.e., a hyperplane that is used to separate two half spaces
CPoint_container	A custom container for points used to build a tree
CSearch_traits	The class `Search_traits` can be used as a template parameter of the kd tree and the search classes
CSearch_traits_2	The class `Search_traits_2` can be used as a template parameter of the kd tree and the search classes
CSearch_traits_3	The class `Search_traits_3` can be used as a template parameter of the kd tree and the search classes
CSearch_traits_adapter	The class `Search_traits_adapter` can be used as a template parameter of the kd tree and the search classes
CSearch_traits_d	The class `Search_traits_d` can be used as a template parameter of the kd tree and the search classes
CSliding_fair	Implements the sliding fair splitting rule
CSliding_midpoint	Implements the sliding midpoint splitting rule
CWeighted_Minkowski_distance	The class `Weighted_Minkowski_distance` provides an implementation of the concept `OrthogonalDistance`, with a weighted Minkowski metric on \( d\)-dimensional points defined by \( l_p(w)(r,q)= ({\Sigma_{i=1}^{i=d} \, w_i(r_i-q_i)^p})^{1/p}\) for \( 0 < p <\infty\) and defined by \( l_{\infty}(w)(r,q)=max \{w_i \|r_i-q_i\| \mid 1 \leq i \leq d\}\)
CFuzzyQueryItem	The concept `FuzzyQueryItem` describes the requirements for fuzzy `d`-dimensional spatial objects
CGeneralDistance	Requirements of a distance class defining a distance between a query item denoting a spatial object and a point. To optimize distance computations transformed distances are used, e.g., for a Euclidean distance the transformed distance is the squared Euclidean distance
COrthogonalDistance	Requirements of an orthogonal distance class supporting incremental distance updates. To optimize distance computations transformed distances are used. E.g., for an Euclidean distance the transformed distance is the squared Euclidean distance
CRangeSearchTraits	The concept `RangeSearchTraits` defines the requirements for the template parameter of the search classes. This concept also defines requirements to range search queries in a model of `SpatialTree`
CSearchGeomTraits_2	The concept `SearchGeomTraits_2` defines the requirements for the template parameter of the search traits classes
CSearchGeomTraits_3	The concept `SearchGeomTraits_3` defines the requirements for the template parameter of the search traits classes
CSearchTraits	The concept `SearchTraits` defines the requirements for the template parameter of the search classes
CSpatialSeparator	The concept `SpatialSeparator` defines the requirements for a separator
CSpatialTree	The concept `SpatialTree` defines the requirements for a tree supporting both neighbor searching and approximate range searching
CSplitter	This is an advanced concept. Advanced The concept `Splitter` defines the requirements for a function object class implementing a splitting rule.