Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12345]

►C_Base
CPonca::BasketComputeObject< _Derived, _Base >	Base ComputeObject for the Basket classes
►C_NeighborhoodFrame
►CPonca::NoWeightFuncBase< DataPoint, _NeighborhoodFrame >	Base Weighting function that set uniform weight to all samples
CPonca::NeighborFilterStoreNormal< DataPoint, NoWeightFunc< DataPoint > >
CPonca::StaticKdTreeBase< Traits >::Buffers	Internal structure storing all the buffers used by the KdTree
►CPonca::CenteredNeighborhoodFrame< DataPoint >	NeighborhoodFrame that express 3d points relatively to a prescribed center
CPonca::DistWeightFunc< DataPoint, WeightKernel >	Weight neighbors according to the euclidean distance between a query and a reference position
CPonca::internal::CNCEigen< DataPoint >	This class contains some stand-alone CorrectedNormalCurrent formulas for triangles, using eigen as linear algebra backend
►CPonca::ComputeObject< Derived >	ComputeObject is a virtual object that represents an algorithm which can be used with the compute functions
►CPonca::BasketComputeObject< Basket< P, NF, Ext0, Exts... >, internal::BasketAggregate< P, NF, Ext0, Exts... >::type >
CPonca::Basket< P, NF, Ext0, Exts >	Aggregator class used to declare specialized structures using CRTP
►CPonca::BasketComputeObject< BasketDiff< BasketType, Type, Ext0, Exts... >, internal::BasketDiffAggregate< BasketType, Type, Ext0, Exts... >::type >
CPonca::BasketDiff< BasketType, Type, Ext0, Exts >	Aggregator class used to declare specialized structures with derivatives computations, using CRTP
CPonca::CNC< P, _method >	Corrected Normal Current Fit type
►CPonca::ComputeObject< _Derived >
CPonca::BasketComputeObject< _Derived, _Base >	Base ComputeObject for the Basket classes
CPonca::ComputeObject< Basket< P, NF, Ext0, Exts... > >
CPonca::ComputeObject< BasketDiff< BasketType, Type, Ext0, Exts... > >
CPonca::ComputeObject< CNC< P, UniformGeneration > >
CPonca::KdTreeBase< Traits >::DefaultConverter	Convert a custom point container to the KdTree PointContainer using DataPoint default constructor
CPonca::QueryOutputBase::DummyOutputParameter
►Cstd::false_type
CPonca::hasFirstFundamentalForm< T, typename >	\FIXME create a macro to automatically generate the testing functions
CPonca::hasNormal< T, typename >	Utility structure used to detect if a Point has a normal field
►CConcept::FittingExtensionConcept
CPonca::CovarianceFitDer< DataPoint, _NFilter, DiffType, T >	Internal generic class computing the derivatives of covariance matrix computed by CovarianceFitBase
CPonca::CovariancePlaneDerImpl< DataPoint, _NFilter, DiffType, T >	[CovariancePlaneFit Definition]
CPonca::GLSDer< DataPoint, _NFilter, DiffType, T >	Differentiation of GLSParam
CPonca::GLSParam< DataPoint, _NFilter, T >	Growing Least Squares reparametrization of the OrientedSphereFit
CPonca::MlsSphereFitDer< DataPoint, _NFilter, DiffType, T >	Extension performing derivation of the mls surface
CPonca::PrimitiveDer< DataPoint, _NFilter, Type, T >	Generic class performing the Fit derivation
►CConcept::FittingProcedureConcept
CPonca::CovarianceFitBase< DataPoint, _NFilter, T >	Procedure that compute and decompose the covariance matrix of the neighbors positions in \(3d\)
CPonca::CovarianceLineFitImpl< DataPoint, _NFilter, T >	Line fitting procedure that minimize the orthogonal distance between the samples and the fitted primitive
CPonca::CovariancePlaneFitImpl< DataPoint, _NFilter, T >	Plane fitting procedure using only points position
CPonca::MeanNormal< DataPoint, _NFilter, T >	Compute the mean normal of the input points
CPonca::MeanNormalDer< DataPoint, _NFilter, DiffType, T >	Compute the derivatives of the input points mean normal
CPonca::MeanPlaneFitImpl< DataPoint, _NFilter, T >	Plane fitting procedure computing the mean position and orientation from oriented points
CPonca::MeanPosition< DataPoint, _NFilter, T >	Compute the barycenter of the input points
CPonca::MeanPositionDer< DataPoint, _NFilter, DiffType, T >	Compute the derivatives of the input points barycenter
CPonca::OrientedSphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on oriented point sets
CPonca::SphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on point set without normals
CPonca::UnorientedSphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on point sets with non-oriented normals
CPonca::GlobalNeighborhoodFrame< DataPoint >	NeighborhoodFrame that keep points in the global frame without applying any transformation
►CPonca::internal::HexagramBase< P >
CPonca::internal::TriangleGenerator< AvgHexagramGeneration, P >
CPonca::internal::TriangleGenerator< HexagramGeneration, P >
►CEigen::Hyperplane
CPonca::Plane< DataPoint, _NFilter, T >	Implicit hyperplane defined by an homogeneous vector \(\mathbf{p}\)
CPonca::IndexSquaredDistance< Index, Scalar >	Associates an index with a distance
CPonca::IndexSquaredDistance< Index, DataPoint::Scalar >
CPonca::IndexSquaredDistance< IndexType, Scalar >
CPonca::IndexSquaredDistance< Traits::IndexType, Traits::DataPoint::Scalar >
►CInput_
►CPonca::Query< Input_, Output_ >	Composes the Query object depending on an input type and output type
►CPonca::KNearestIndexQuery< Traits::IndexType, Traits::DataPoint::Scalar >
CPonca::KnnGraphKNearestQuery< Traits >	Extension of the Query class that allows to read the result of a k-nearest neighbors search on the KnnGraph
►CPonca::RangeIndexQuery< Traits::IndexType, Traits::DataPoint::Scalar >
CPonca::KnnGraphRangeQuery< Traits >	Extension of the Query class that allows to read the result of a range neighbor search on the KnnGraph
►CPonca::KdTreeCustomizableNode< Index, NodeIndex, DataPoint, LeafSize, _InnerNodeType, _LeafNodeType >	The node type used by default by the kd-tree
CPonca::KdTreeDefaultNode< Index, NodeIndex, DataPoint, LeafSize >
CPonca::KdTreeCustomizableNode< Index, NodeIndex, DataPoint, Index, KdTreeDefaultInnerNode< NodeIndex, DataPoint::Scalar, DataPoint::Dim >, KdTreeDefaultLeafNode< Index, Index > >
CPonca::KdTreeDefaultInnerNode< NodeIndex, Scalar, DIM >
CPonca::KdTreeDefaultLeafNode< Index, Size >
CPonca::KdTreeDefaultTraits< _DataPoint, _NodeType >	The default traits type used by the kd-tree
CPonca::KdTreeKNearestIterator< Index, DataPoint >	Input iterator to read the KdTreeKNearestQueryBase object
CPonca::KdTreeNearestIterator< Index >	Input iterator to read the KdTreeKNearestQueryBase object
CPonca::KdTreePointerTraits< _DataPoint, _NodeType >	Variant to the KdTree Traits type that uses pointers as internal storage instead of an STL-like container
►CPonca::KdTreeQuery< Traits >	Query object that provides a method to search neighbors on the KdTree depending on a distance threshold
CPonca::KdTreeKNearestQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a k-nearest neighbors search on the KdTree
CPonca::KdTreeNearestQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a nearest neighbor search on the KdTree
CPonca::KdTreeRangeQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a range neighbors search on the KdTree
CPonca::KdTreeRangeIterator< Index, DataPoint, QueryT_ >	Input iterator to read the KdTreeRangeQueryBase object
CPonca::KnnGraphBase< Traits >	Customizable base class for KnnGraph datastructure
CPonca::KnnGraphDefaultTraits< _DataPoint >	The default traits type used by the kd-tree
CPonca::KnnGraphRangeIterator< Traits >	Input iterator to read the KnnGraphRangeQuery object
CPonca::limited_priority_queue< T, CompareT >	The limited_priority_queue class is similar to std::priority_queue but has a limited capacity and handles the comparison differently
CPonca::limited_priority_queue< IndexSquaredDistance< Index, Scalar > >
CPonca::limited_priority_queue< Ponca::IndexSquaredDistance< Index, DataPoint::Scalar > >
CPonca::limited_priority_queue< Ponca::IndexSquaredDistance< Index, Scalar > >
CPonca::limited_priority_queue< Ponca::IndexSquaredDistance< Traits::IndexType, Traits::DataPoint::Scalar > >
►CNeighborFilter
CPonca::NeighborFilterStoreNormal< DataPoint, NeighborFilter >	This class extends a NeighborFilter class to also store the normal of the evaluation point, for use outside the scope of this class
►COutput_
CPonca::Query< Input_, Output_ >	Composes the Query object depending on an input type and output type
►CEigen::ParametrizedLine
CPonca::Line< DataPoint, _NFilter, T >	A parametrized line is defined by an origin point \(\mathbf{o}\) and a unit direction vector \(\overrightarrow{\mathbf{d}}\) such that the line corresponds to the set \(l(t)=\mathbf{o}+t\overrightarrow{\mathbf{d}}, t\in \mathbb{R}\)
CPonca::PointPosition< _Scalar, _Dim >	Point data type containing only containing the position vector
CPonca::PointPositionNormal< _Scalar, _Dim >	Point data type containing the position and normal vectors
CPonca::PointPositionNormalBinding< _Scalar, _Dim >	Variant of the PointPositionNormal data type that uses external raw data
CPonca::PointPositionNormalLazyBinding< _Scalar, _Dim >	Variant of the PointPositionNormal data type that uses external raw data.
CPonca::PrimitiveBase< DataPoint, _NFilter, T >	Primitive base class
►CPonca::QueryInputBase	Base class for queries input type
CPonca::QueryInput< Traits::IndexType >
►CPonca::QueryInput< Index >
►CPonca::QueryInputIsIndex< Index >	Extension of QueryInput that handles an index based search, in a partitioning structure
►CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsKNearest< Index, Scalar > >
CPonca::KNearestIndexQuery< Index, Scalar >	Base Query class combining QueryInputIsIndex and QueryOutputIsKNearest
►CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsNearest< Index, Scalar > >
CPonca::NearestIndexQuery< Index, Scalar >	Base Query class combining QueryInputIsIndex and QueryOutputIsNearest
►CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsRange< Index, Scalar > >
CPonca::RangeIndexQuery< Index, Scalar >	Base Query class combining QueryInputIsIndex and QueryOutputIsRange
►CPonca::QueryInput< DataPoint::VectorType >
►CPonca::QueryInputIsPosition< DataPoint >	Extension of QueryInput that handles a position based search, in a partitioning structure
►CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsKNearest< Index, DataPoint::Scalar > >
CPonca::KNearestPointQuery< Index, DataPoint >	Base Query class combining QueryInputIsPosition and QueryOutputIsKNearest
►CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsNearest< Index, DataPoint::Scalar > >
CPonca::NearestPointQuery< Index, DataPoint >	Base Query class combining QueryInputIsPosition and QueryOutputIsNearest
►CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsRange< Index, DataPoint::Scalar > >
CPonca::RangePointQuery< Index, DataPoint >	Base Query class combining QueryInputIsPosition and QueryOutputIsRange
►CPonca::QueryInput< InputType_ >	Base class for Query input type
►CPonca::QueryInputIsIndex< Traits::IndexType >
CPonca::Query< QueryInputIsIndex< Traits::IndexType >, QueryOutputIsKNearest< Traits::IndexType, Traits::DataPoint::Scalar > >
CPonca::Query< QueryInputIsIndex< Traits::IndexType >, QueryOutputIsRange< Traits::IndexType, Traits::DataPoint::Scalar > >
►CPonca::QueryOutputBase	Base class for queries output types
►CPonca::QueryOutputIsKNearest< Index, DataPoint::Scalar >
CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsKNearest< Index, DataPoint::Scalar > >
►CPonca::QueryOutputIsKNearest< Traits::IndexType, Traits::DataPoint::Scalar >
CPonca::Query< QueryInputIsIndex< Traits::IndexType >, QueryOutputIsKNearest< Traits::IndexType, Traits::DataPoint::Scalar > >
►CPonca::QueryOutputIsNearest< Index, DataPoint::Scalar >
CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsNearest< Index, DataPoint::Scalar > >
►CPonca::QueryOutputIsRange< Traits::IndexType, Traits::DataPoint::Scalar >
CPonca::Query< QueryInputIsIndex< Traits::IndexType >, QueryOutputIsRange< Traits::IndexType, Traits::DataPoint::Scalar > >
►CPonca::QueryOutputIsRange< Index, DataPoint::Scalar >
CPonca::Query< QueryInputIsPosition< DataPoint >, QueryOutputIsRange< Index, DataPoint::Scalar > >
►CPonca::QueryOutputIsKNearest< Index, Scalar >	Class to construct the knearest queries
CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsKNearest< Index, Scalar > >
►CPonca::QueryOutputIsNearest< Index, Scalar >	Class to construct the nearest query output
CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsNearest< Index, Scalar > >
►CPonca::QueryOutputIsRange< Index, Scalar >	Class to construct the range query output
CPonca::Query< QueryInputIsIndex< Index >, QueryOutputIsRange< Index, Scalar > >
►CQueryType
CPonca::KdTreeKNearestQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a k-nearest neighbors search on the KdTree
CPonca::KdTreeNearestQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a nearest neighbor search on the KdTree
CPonca::KdTreeRangeQueryBase< Traits, IteratorType, QueryType >	Extension of the Query class that allows to read the result of a range neighbors search on the KdTree
CPonca::internal::CNCEigen< DataPoint >::SphericalTriangle	Represents a triangle on a sphere of radius one
CPonca::Stack< T, N >	Stack with fixed-size storage
CPonca::Stack< Ponca::IndexSquaredDistance< IndexType, Scalar >, 2 *Traits::MAX_DEPTH >
►CPonca::StaticKdTreeBase< Traits >	Customizable static base class for KdTree datastructure implementations
►CPonca::KdTreeBase< KdTreeDefaultTraits< DataPoint > >
CPonca::KdTree< DataPoint >	Abstract KdTree type with KdTreeDefaultTraits
►CPonca::KdTreeBase< Traits >
►CPonca::KdTreeDenseBase< KdTreeDefaultTraits< DataPoint > >
CPonca::KdTreeDense< DataPoint >	Public interface for dense KdTree datastructure
►CPonca::KdTreeSparseBase< KdTreeDefaultTraits< DataPoint > >
CPonca::KdTreeSparse< DataPoint >	Public interface for sparse KdTree datastructure
CPonca::KdTreeDenseBase< Traits >	Customizable base class for dense KdTree datastructure
CPonca::KdTreeSparseBase< Traits >	Customizable base class for KdTreeSparse datastructure
►CPonca::StaticKdTreeBase< KdTreeDefaultTraits< DataPoint > >
CPonca::StaticKdTree< DataPoint >	A KdTree type with KdTreeDefaultTraits that doesn't define the build function
►CT
CPonca::AlgebraicSphere< DataPoint, _NFilter, T >	Algebraic Sphere primitive
CPonca::CovarianceFitBase< DataPoint, _NFilter, T >	Procedure that compute and decompose the covariance matrix of the neighbors positions in \(3d\)
CPonca::CovarianceFitDer< DataPoint, _NFilter, DiffType, T >	Internal generic class computing the derivatives of covariance matrix computed by CovarianceFitBase
CPonca::CovarianceLineFitImpl< DataPoint, _NFilter, T >	Line fitting procedure that minimize the orthogonal distance between the samples and the fitted primitive
CPonca::CovariancePlaneDerImpl< DataPoint, _NFilter, DiffType, T >	[CovariancePlaneFit Definition]
CPonca::CovariancePlaneFitImpl< DataPoint, _NFilter, T >	Plane fitting procedure using only points position
CPonca::DryFit< DataPoint, _NFilter, T >	Empty fitting object doing no computation
CPonca::FundamentalFormWeingartenEstimator< DataPoint, _NFilter, T >	Compute a Weingarten map from fundamental forms
CPonca::GLSDer< DataPoint, _NFilter, DiffType, T >	Differentiation of GLSParam
CPonca::GLSParam< DataPoint, _NFilter, T >	Growing Least Squares reparametrization of the OrientedSphereFit
CPonca::HeightField< DataPoint, _NFilter, T >	Internal base classe for height fields
CPonca::Line< DataPoint, _NFilter, T >	A parametrized line is defined by an origin point \(\mathbf{o}\) and a unit direction vector \(\overrightarrow{\mathbf{d}}\) such that the line corresponds to the set \(l(t)=\mathbf{o}+t\overrightarrow{\mathbf{d}}, t\in \mathbb{R}\)
CPonca::MeanNormal< DataPoint, _NFilter, T >	Compute the mean normal of the input points
CPonca::MeanNormalDer< DataPoint, _NFilter, DiffType, T >	Compute the derivatives of the input points mean normal
CPonca::MeanPlaneFitImpl< DataPoint, _NFilter, T >	Plane fitting procedure computing the mean position and orientation from oriented points
CPonca::MeanPosition< DataPoint, _NFilter, T >	Compute the barycenter of the input points
CPonca::MeanPositionDer< DataPoint, _NFilter, DiffType, T >	Compute the derivatives of the input points barycenter
CPonca::MlsSphereFitDer< DataPoint, _NFilter, DiffType, T >	Extension performing derivation of the mls surface
CPonca::MongePatch< DataPoint, _NFilter, T >	Monge Patch primitive, defined as \( \mathbf{x}(u,v)= (u,v,h(u,v)) \), with \(h(u,v)\) defined by a Base class
CPonca::MongePatchQuadraticFitImpl< DataPoint, _NFilter, T >	Extension to compute the best fit quadric on 3d points expressed as \(f(u,v)=h\)
CPonca::MongePatchRestrictedQuadraticFitImpl< DataPoint, _NFilter, T >	Extension to compute the best fit restricted quadric on 3d points expressed as \(f(u,v)=h\)
CPonca::NormalDerivativeWeingartenEstimator< DataPoint, _NFilter, DiffType, T >	Compute a Weingarten map from the spatial derivatives of the normal field \( N \)
CPonca::OrientedSphereDerImpl< DataPoint, _NFilter, DiffType, T >	[OrientedSphereFit Definition]
CPonca::OrientedSphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on oriented point sets
CPonca::Plane< DataPoint, _NFilter, T >	Implicit hyperplane defined by an homogeneous vector \(\mathbf{p}\)
CPonca::PrimitiveDer< DataPoint, _NFilter, Type, T >	Generic class performing the Fit derivation
CPonca::QuadraticHeightField< DataPoint, _NFilter, T >	Quadratic height field defined as \(h(u,v)=h_{uu}u^2 + h_{vv}v^2 + h_{uv}uv + h_u u + h_v v + h_c \)
CPonca::RestrictedQuadraticHeightField< DataPoint, _NFilter, T >	Quadratic height field defined as \(h(u,v)=h_{uu}u^2 + h_{vv}v^2 + h_{uv}uv + h_c \)
CPonca::SphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on point set without normals
CPonca::UnorientedSphereDerImpl< DataPoint, _NFilter, DiffType, T >
CPonca::UnorientedSphereFitImpl< DataPoint, _NFilter, T >	Algebraic Sphere fitting procedure on point sets with non-oriented normals
►CPonca::internal::CurvatureEstimatorBase< DataPoint, _NFilter, T >	Base class for any 3d curvature estimator: holds \(k_{\min}\), \(k_{\max}\) and associated vectors, such that \( k_{\min} <= k_{\max} \)
CPonca::CurvatureEstimator< DataPoint, _NFilter, T >	Make CurvatureEstimatorBase available to standard Basket object
CPonca::CurvatureEstimatorDer< DataPoint, _NFilter, DiffType, T >	Make CurvatureEstimatorBase available to BasketDiff object
►CPonca::internal::WeingartenCurvatureEstimatorBase< DataPoint, _NFilter, T >	Compute principal curvatures from a base class providing fundamental forms
CPonca::WeingartenCurvatureEstimator< DataPoint, _NFilter, T >	Compute principal curvatures from a base class providing fundamental forms.
CPonca::WeingartenCurvatureEstimatorDer< DataPoint, _NFilter, DiffType, T >	Compute principal curvatures from a base class providing fundamental forms.
CPonca::internal::Triangle< DataPoint >	Stores the three points and normals of the triangles and provides access to Corrected Normal Current formula
CPonca::internal::TriangleGenerator< Method, P >
CPonca::internal::TriangleGenerator< IndependentGeneration, P >
CPonca::internal::TriangleGenerator< UniformGeneration, P >
►Cstd::true_type
CPonca::hasFirstFundamentalForm< T, std::void_t< decltype(std::declval< T >().firstFundamentalForm())> >
CPonca::hasNormal< T, std::void_t< decltype(std::declval< T >().normal())> >
►Cinternal::BasketAggregate::type
CPonca::BasketComputeObject< Basket< P, NF, Ext0, Exts... >, internal::BasketAggregate< P, NF, Ext0, Exts... >::type >
►Cinternal::BasketDiffAggregate::type
CPonca::BasketComputeObject< BasketDiff< BasketType, Type, Ext0, Exts... >, internal::BasketDiffAggregate< BasketType, Type, Ext0, Exts... >::type >
►CConcept::WeightKernelConcept
CPonca::CompactExpWeightKernel< _Scalar >	Compact Exponential WeightKernel defined in \(\left[0 : 1\right]\)
CPonca::ConstantWeightKernel< _Scalar >	Concept::WeightKernelConcept returning a constant value
CPonca::GaussianWeightKernel< _Scalar >	Non-compact Gaussian WeightKernel
CPonca::PolynomialSmoothWeightKernel< _Scalar, m, n >	Compact generalised version of SmoothWeightKernel with arbitrary degrees : \( w(x)=(x^n-1)^m \)
CPonca::SingularWeightKernel< _Scalar >	Compact singular WeightKernel defined in \(\left]0 : 1\right]\)
CPonca::SmoothWeightKernel< _Scalar >	Compact smooth WeightKernel of 2nd degree, defined in \(\left[0 : 1\right]\) Special case of PolynomialSmoothWeightKernel<Scalar, 2, 2>
CPonca::WendlandWeightKernel< _Scalar >	Compact Wendland WeightKernel defined in \(\left[0 : 1\right]\)