libnabo  1.0.4
Classes | Public Types | Protected Types | Protected Member Functions | Protected Attributes | List of all members
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap > Struct Template Reference

KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation. More...

#include <nabo_private.h>

Inheritance diagram for Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >:
Nabo::NearestNeighbourSearch< T >

Classes

struct  BucketEntry
 entry in a bucket More...
 
struct  Node
 search node More...
 

Public Types

typedef NearestNeighbourSearch
< T >::Vector 
Vector
 
typedef NearestNeighbourSearch
< T >::Matrix 
Matrix
 
typedef NearestNeighbourSearch
< T >::Index 
Index
 
typedef NearestNeighbourSearch
< T >::IndexVector 
IndexVector
 
typedef NearestNeighbourSearch
< T >::IndexMatrix 
IndexMatrix
 
- Public Types inherited from Nabo::NearestNeighbourSearch< T >
enum  SearchType {
  BRUTE_FORCE = 0, KDTREE_LINEAR_HEAP, KDTREE_TREE_HEAP, KDTREE_CL_PT_IN_NODES,
  KDTREE_CL_PT_IN_LEAVES, BRUTE_FORCE_CL, SEARCH_TYPE_COUNT
}
 type of search More...
 
enum  CreationOptionFlags { TOUCH_STATISTICS = 1 }
 creation option More...
 
enum  SearchOptionFlags { ALLOW_SELF_MATCH = 1, SORT_RESULTS = 2 }
 search option More...
 
typedef Eigen::Matrix< T,
Eigen::Dynamic, 1 > 
Vector
 an Eigen vector of type T, to hold the coordinates of a point
 
typedef Eigen::Matrix< T,
Eigen::Dynamic, Eigen::Dynamic > 
Matrix
 a column-major Eigen matrix in which each column is a point; this matrix has dim rows
 
typedef int Index
 an index to a Vector or a Matrix, for refering to data points
 
typedef Eigen::Matrix< Index,
Eigen::Dynamic, 1 > 
IndexVector
 a vector of indices to data points
 
typedef Eigen::Matrix< Index,
Eigen::Dynamic, Eigen::Dynamic > 
IndexMatrix
 a matrix of indices to data points
 

Protected Types

typedef std::vector< IndexBuildPoints
 indices of points during kd-tree construction
 
typedef BuildPoints::iterator BuildPointsIt
 iterator to indices of points during kd-tree construction
 
typedef BuildPoints::const_iterator BuildPointsCstIt
 const-iterator to indices of points during kd-tree construction
 
typedef std::vector< NodeNodes
 dense vector of search nodes, provides better memory performances than many small objects
 
typedef std::vector< BucketEntryBuckets
 bucket data
 

Protected Member Functions

uint32_t createDimChildBucketSize (const uint32_t dim, const uint32_t childIndex) const
 create the compound index containing the dimension and the index of the child or the bucket size
 
uint32_t getDim (const uint32_t dimChildBucketSize) const
 get the dimension out of the compound index
 
uint32_t getChildBucketSize (const uint32_t dimChildBucketSize) const
 get the child index or the bucket size out of the coumpount index
 
- Protected Member Functions inherited from Nabo::NearestNeighbourSearch< T >
 NearestNeighbourSearch (const Matrix &cloud, const Index dim, const unsigned creationOptionFlags)
 constructor
 
void checkSizesKnn (const Matrix &query, const IndexMatrix &indices, const Matrix &dists2, const Index k, const unsigned optionFlags, const Vector *maxRadii=0) const
 Make sure that the output matrices have the right sizes. Throw an exception otherwise. More...
 

Protected Attributes

const unsigned bucketSize
 size of bucket
 
const uint32_t dimBitCount
 number of bits required to store dimension index + number of dimensions
 
const uint32_t dimMask
 mask to access dim
 
Nodes nodes
 search nodes
 
Buckets buckets
 buckets
 
std::pair< T, T > getBounds (const BuildPointsIt first, const BuildPointsIt last, const unsigned dim)
 return the bounds of points from [first..last[ on dimension dim
 
unsigned buildNodes (const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues)
 construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues]
 
unsigned long onePointKnn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, int i, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const
 search one point, call recurseKnn with the correct template parameters More...
 
template<bool allowSelfMatch, bool collectStatistics>
unsigned long recurseKnn (const T *query, const unsigned n, T rd, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2) const
 recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993] More...
 
 KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt (const Matrix &cloud, const Index dim, const unsigned creationOptionFlags, const Parameters &additionalParameters)
 constructor, calls NearestNeighbourSearch<T>(cloud)
 
virtual unsigned long knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const
 Find the k nearest neighbours for each point of query. More...
 
virtual unsigned long knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Vector &maxRadii, const Index k=1, const T epsilon=0, const unsigned optionFlags=0) const
 Find the k nearest neighbours for each point of query. More...
 

Additional Inherited Members

- Public Member Functions inherited from Nabo::NearestNeighbourSearch< T >
unsigned long knn (const Vector &query, IndexVector &indices, Vector &dists2, const Index k=1, const T epsilon=0, const unsigned optionFlags=0, const T maxRadius=std::numeric_limits< T >::infinity()) const
 Find the k nearest neighbours of query. More...
 
virtual ~NearestNeighbourSearch ()
 virtual destructor
 
- Static Public Member Functions inherited from Nabo::NearestNeighbourSearch< T >
static NearestNeighbourSearchcreate (const Matrix &cloud, const Index dim=std::numeric_limits< Index >::max(), const SearchType preferedType=KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
 Create a nearest-neighbour search. More...
 
static NearestNeighbourSearchcreateBruteForce (const Matrix &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0)
 Create a nearest-neighbour search, using brute-force search, useful for comparison only. More...
 
static NearestNeighbourSearchcreateKDTreeLinearHeap (const Matrix &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
 Create a nearest-neighbour search, using a kd-tree with linear heap, good for small k (~up to 30) More...
 
static NearestNeighbourSearchcreateKDTreeTreeHeap (const Matrix &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters())
 Create a nearest-neighbour search, using a kd-tree with tree heap, good for large k (~from 30) More...
 
- Public Attributes inherited from Nabo::NearestNeighbourSearch< T >
const Matrixcloud
 the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeighbourSearch object
 
const Index dim
 the dimensionality of the data-point cloud
 
const unsigned creationOptionFlags
 creation options
 
const Vector minBound
 the low bound of the search space (axis-aligned bounding box)
 
const Vector maxBound
 the high bound of the search space (axis-aligned bounding box)
 

Detailed Description

template<typename T, typename Heap>
struct Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >

KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation.

Member Function Documentation

template<typename T , typename Heap >
unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >::knn ( const Matrix query,
IndexMatrix indices,
Matrix dists2,
const Index  k,
const T  epsilon,
const unsigned  optionFlags,
const T  maxRadius 
) const
virtual

Find the k nearest neighbours for each point of query.

If the search finds less than k points, the empty entries in dists2 will be filled with infinity and the indices with 0.

Parameters
queryquery points
indicesindices of nearest neighbours, must be of size k x query.cols()
dists2squared distances to nearest neighbours, must be of size k x query.cols()
knumber of nearest neighbour requested
epsilonmaximal ratio of error for approximate search, 0 for exact search; has no effect if the number of neighbour found is smaller than the number requested
optionFlagssearch options, a bitwise OR of elements of SearchOptionFlags
maxRadiusmaximum radius in which to search, can be used to prune search, is not affected by epsilon
Returns
if creationOptionFlags contains TOUCH_STATISTICS, return the number of point touched, otherwise return 0

Implements Nabo::NearestNeighbourSearch< T >.

template<typename T , typename Heap >
unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >::knn ( const Matrix query,
IndexMatrix indices,
Matrix dists2,
const Vector maxRadii,
const Index  k = 1,
const T  epsilon = 0,
const unsigned  optionFlags = 0 
) const
virtual

Find the k nearest neighbours for each point of query.

If the search finds less than k points, the empty entries in dists2 will be filled with infinity and the indices with 0.

Parameters
queryquery points
indicesindices of nearest neighbours, must be of size k x query.cols()
dists2squared distances to nearest neighbours, must be of size k x query.cols()
maxRadiivector of maximum radii in which to search, used to prune search, is not affected by epsilon
knumber of nearest neighbour requested
epsilonmaximal ratio of error for approximate search, 0 for exact search; has no effect if the number of neighbour found is smaller than the number requested
optionFlagssearch options, a bitwise OR of elements of SearchOptionFlags
Returns
if creationOptionFlags contains TOUCH_STATISTICS, return the number of point touched, otherwise return 0

Implements Nabo::NearestNeighbourSearch< T >.

template<typename T , typename Heap >
unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >::onePointKnn ( const Matrix query,
IndexMatrix indices,
Matrix dists2,
int  i,
Heap &  heap,
std::vector< T > &  off,
const T  maxError,
const T  maxRadius2,
const bool  allowSelfMatch,
const bool  collectStatistics,
const bool  sortResults 
) const
protected

search one point, call recurseKnn with the correct template parameters

Parameters
querypointer to query coordinates
indicesindices of nearest neighbours, must be of size k x query.cols()
dists2squared distances to nearest neighbours, must be of size k x query.cols()
iindex of point to search
heapreference to heap
offreference to array of offsets
maxErrorerror factor (1 + epsilon)
maxRadius2square of maximum radius
allowSelfMatchwhether to allow self match
collectStatisticswhether to collect statistics
sortResultswether to sort results
template<typename T , typename Heap >
template<bool allowSelfMatch, bool collectStatistics>
unsigned long Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt< T, Heap >::recurseKnn ( const T *  query,
const unsigned  n,
rd,
Heap &  heap,
std::vector< T > &  off,
const T  maxError,
const T  maxRadius2 
) const
protected

recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993]

Parameters
querypointer to query coordinates
nindex of node to visit
rdsquared dist to this rect
heapreference to heap
offreference to array of offsets
maxErrorerror factor (1 + epsilon)
maxRadius2square of maximum radius

The documentation for this struct was generated from the following files: