Added missing standard vcg header comment
This commit is contained in:
parent
d520fe2f0e
commit
c2f98a8595
|
|
@ -1,3 +1,26 @@
|
|||
/****************************************************************************
|
||||
* VCGLib o o *
|
||||
* Visual and Computer Graphics Library o o *
|
||||
* _ O _ *
|
||||
* Copyright(C) 2004 \/)\/ *
|
||||
* Visual Computing Lab /\/| *
|
||||
* ISTI - Italian National Research Council | *
|
||||
* \ *
|
||||
* All rights reserved. *
|
||||
* *
|
||||
* This program is free software; you can redistribute it and/or modify *
|
||||
* it under the terms of the GNU General Public License as published by *
|
||||
* the Free Software Foundation; either version 2 of the License, or *
|
||||
* (at your option) any later version. *
|
||||
* *
|
||||
* This program is distributed in the hope that it will be useful, *
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of *
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *
|
||||
* GNU General Public License (http://www.gnu.org/licenses/gpl.txt) *
|
||||
* for more details. *
|
||||
* *
|
||||
****************************************************************************/
|
||||
|
||||
#ifndef KDTREE_VCG_H
|
||||
#define KDTREE_VCG_H
|
||||
|
||||
|
|
@ -11,476 +34,476 @@
|
|||
|
||||
namespace vcg {
|
||||
|
||||
template<typename _DataType>
|
||||
class ConstDataWrapper
|
||||
{
|
||||
public:
|
||||
typedef _DataType DataType;
|
||||
inline ConstDataWrapper()
|
||||
: mpData(0), mStride(0), mSize(0)
|
||||
{}
|
||||
inline ConstDataWrapper(const DataType* pData, int size, int stride = sizeof(DataType))
|
||||
: mpData(reinterpret_cast<const unsigned char*>(pData)), mStride(stride), mSize(size)
|
||||
{}
|
||||
inline const DataType& operator[] (int i) const
|
||||
{
|
||||
return *reinterpret_cast<const DataType*>(mpData + i*mStride);
|
||||
}
|
||||
inline size_t size() const { return mSize; }
|
||||
protected:
|
||||
const unsigned char* mpData;
|
||||
int mStride;
|
||||
size_t mSize;
|
||||
};
|
||||
template<typename _DataType>
|
||||
class ConstDataWrapper
|
||||
{
|
||||
public:
|
||||
typedef _DataType DataType;
|
||||
inline ConstDataWrapper()
|
||||
: mpData(0), mStride(0), mSize(0)
|
||||
{}
|
||||
inline ConstDataWrapper(const DataType* pData, int size, int stride = sizeof(DataType))
|
||||
: mpData(reinterpret_cast<const unsigned char*>(pData)), mStride(stride), mSize(size)
|
||||
{}
|
||||
inline const DataType& operator[] (int i) const
|
||||
{
|
||||
return *reinterpret_cast<const DataType*>(mpData + i*mStride);
|
||||
}
|
||||
inline size_t size() const { return mSize; }
|
||||
protected:
|
||||
const unsigned char* mpData;
|
||||
int mStride;
|
||||
size_t mSize;
|
||||
};
|
||||
|
||||
template<class StdVectorType>
|
||||
class VectorConstDataWrapper :public ConstDataWrapper<typename StdVectorType::value_type>
|
||||
{
|
||||
public:
|
||||
inline VectorConstDataWrapper(StdVectorType &vec):
|
||||
ConstDataWrapper<typename StdVectorType::value_type> ( &(vec[0]), vec.size(), sizeof(typename StdVectorType::value_type))
|
||||
{}
|
||||
};
|
||||
template<class StdVectorType>
|
||||
class VectorConstDataWrapper :public ConstDataWrapper<typename StdVectorType::value_type>
|
||||
{
|
||||
public:
|
||||
inline VectorConstDataWrapper(StdVectorType &vec):
|
||||
ConstDataWrapper<typename StdVectorType::value_type> ( &(vec[0]), vec.size(), sizeof(typename StdVectorType::value_type))
|
||||
{}
|
||||
};
|
||||
|
||||
template<class MeshType>
|
||||
class VertexConstDataWrapper :public ConstDataWrapper<typename MeshType::CoordType>
|
||||
{
|
||||
public:
|
||||
inline VertexConstDataWrapper(MeshType &m):
|
||||
ConstDataWrapper<typename MeshType::CoordType> ( &(m.vert[0].P()), m.vert.size(), sizeof(typename MeshType::VertexType))
|
||||
{}
|
||||
};
|
||||
template<class MeshType>
|
||||
class VertexConstDataWrapper :public ConstDataWrapper<typename MeshType::CoordType>
|
||||
{
|
||||
public:
|
||||
inline VertexConstDataWrapper(MeshType &m):
|
||||
ConstDataWrapper<typename MeshType::CoordType> ( &(m.vert[0].P()), m.vert.size(), sizeof(typename MeshType::VertexType))
|
||||
{}
|
||||
};
|
||||
|
||||
/**
|
||||
* This class allows to create a Kd-Tree thought to perform the neighbour query (radius search, knn-nearest serach and closest search).
|
||||
* The class implemetantion is thread-safe.
|
||||
*/
|
||||
template<typename _Scalar>
|
||||
class KdTree
|
||||
{
|
||||
public:
|
||||
/**
|
||||
* This class allows to create a Kd-Tree thought to perform the neighbour query (radius search, knn-nearest serach and closest search).
|
||||
* The class implemetantion is thread-safe.
|
||||
*/
|
||||
template<typename _Scalar>
|
||||
class KdTree
|
||||
{
|
||||
public:
|
||||
|
||||
typedef _Scalar Scalar;
|
||||
typedef vcg::Point3<Scalar> VectorType;
|
||||
typedef vcg::Box3<Scalar> AxisAlignedBoxType;
|
||||
typedef _Scalar Scalar;
|
||||
typedef vcg::Point3<Scalar> VectorType;
|
||||
typedef vcg::Box3<Scalar> AxisAlignedBoxType;
|
||||
|
||||
typedef HeapMaxPriorityQueue<int, Scalar> PriorityQueue;
|
||||
typedef HeapMaxPriorityQueue<int, Scalar> PriorityQueue;
|
||||
|
||||
struct Node
|
||||
{
|
||||
union {
|
||||
//standard node
|
||||
struct {
|
||||
Scalar splitValue;
|
||||
unsigned int firstChildId:24;
|
||||
unsigned int dim:2;
|
||||
unsigned int leaf:1;
|
||||
};
|
||||
//leaf
|
||||
struct {
|
||||
unsigned int start;
|
||||
unsigned short size;
|
||||
};
|
||||
};
|
||||
};
|
||||
typedef std::vector<Node> NodeList;
|
||||
struct Node
|
||||
{
|
||||
union {
|
||||
//standard node
|
||||
struct {
|
||||
Scalar splitValue;
|
||||
unsigned int firstChildId:24;
|
||||
unsigned int dim:2;
|
||||
unsigned int leaf:1;
|
||||
};
|
||||
//leaf
|
||||
struct {
|
||||
unsigned int start;
|
||||
unsigned short size;
|
||||
};
|
||||
};
|
||||
};
|
||||
typedef std::vector<Node> NodeList;
|
||||
|
||||
// return the protected members which store the nodes and the points list
|
||||
inline const NodeList& _getNodes(void) { return mNodes; }
|
||||
inline const std::vector<VectorType>& _getPoints(void) { return mPoints; }
|
||||
// return the protected members which store the nodes and the points list
|
||||
inline const NodeList& _getNodes(void) { return mNodes; }
|
||||
inline const std::vector<VectorType>& _getPoints(void) { return mPoints; }
|
||||
|
||||
public:
|
||||
public:
|
||||
|
||||
KdTree(const ConstDataWrapper<VectorType>& points, unsigned int nofPointsPerCell = 16, unsigned int maxDepth = 64);
|
||||
KdTree(const ConstDataWrapper<VectorType>& points, unsigned int nofPointsPerCell = 16, unsigned int maxDepth = 64);
|
||||
|
||||
~KdTree();
|
||||
~KdTree();
|
||||
|
||||
void doQueryK(const VectorType& queryPoint, int k, PriorityQueue& mNeighborQueue);
|
||||
void doQueryK(const VectorType& queryPoint, int k, PriorityQueue& mNeighborQueue);
|
||||
|
||||
void doQueryDist(const VectorType& queryPoint, float dist, std::vector<unsigned int>& points, std::vector<Scalar>& sqrareDists);
|
||||
void doQueryDist(const VectorType& queryPoint, float dist, std::vector<unsigned int>& points, std::vector<Scalar>& sqrareDists);
|
||||
|
||||
void doQueryClosest(const VectorType& queryPoint, unsigned int& index, Scalar& dist);
|
||||
void doQueryClosest(const VectorType& queryPoint, unsigned int& index, Scalar& dist);
|
||||
|
||||
protected:
|
||||
protected:
|
||||
|
||||
// element of the stack
|
||||
struct QueryNode
|
||||
{
|
||||
QueryNode() {}
|
||||
QueryNode(unsigned int id) : nodeId(id) {}
|
||||
unsigned int nodeId; // id of the next node
|
||||
Scalar sq; // squared distance to the next node
|
||||
};
|
||||
// element of the stack
|
||||
struct QueryNode
|
||||
{
|
||||
QueryNode() {}
|
||||
QueryNode(unsigned int id) : nodeId(id) {}
|
||||
unsigned int nodeId; // id of the next node
|
||||
Scalar sq; // squared distance to the next node
|
||||
};
|
||||
|
||||
// used to build the tree: split the subset [start..end[ according to dim and splitValue,
|
||||
// and returns the index of the first element of the second subset
|
||||
unsigned int split(int start, int end, unsigned int dim, float splitValue);
|
||||
// used to build the tree: split the subset [start..end[ according to dim and splitValue,
|
||||
// and returns the index of the first element of the second subset
|
||||
unsigned int split(int start, int end, unsigned int dim, float splitValue);
|
||||
|
||||
void createTree(unsigned int nodeId, unsigned int start, unsigned int end, unsigned int level, unsigned int targetCellsize, unsigned int targetMaxDepth);
|
||||
void createTree(unsigned int nodeId, unsigned int start, unsigned int end, unsigned int level, unsigned int targetCellsize, unsigned int targetMaxDepth);
|
||||
|
||||
protected:
|
||||
protected:
|
||||
|
||||
AxisAlignedBoxType mAABB; //BoundingBox
|
||||
NodeList mNodes; //kd-tree nodes
|
||||
std::vector<VectorType> mPoints; //points read from the input DataWrapper
|
||||
std::vector<unsigned int> mIndices; //points indices
|
||||
};
|
||||
AxisAlignedBoxType mAABB; //BoundingBox
|
||||
NodeList mNodes; //kd-tree nodes
|
||||
std::vector<VectorType> mPoints; //points read from the input DataWrapper
|
||||
std::vector<unsigned int> mIndices; //points indices
|
||||
};
|
||||
|
||||
template<typename Scalar>
|
||||
KdTree<Scalar>::KdTree(const ConstDataWrapper<VectorType>& points, unsigned int nofPointsPerCell, unsigned int maxDepth)
|
||||
: mPoints(points.size()), mIndices(points.size())
|
||||
{
|
||||
// compute the AABB of the input
|
||||
mPoints[0] = points[0];
|
||||
mAABB.Set(mPoints[0]);
|
||||
for (unsigned int i=1 ; i<mPoints.size() ; ++i)
|
||||
{
|
||||
mPoints[i] = points[i];
|
||||
mIndices[i] = i;
|
||||
mAABB.Add(mPoints[i]);
|
||||
}
|
||||
template<typename Scalar>
|
||||
KdTree<Scalar>::KdTree(const ConstDataWrapper<VectorType>& points, unsigned int nofPointsPerCell, unsigned int maxDepth)
|
||||
: mPoints(points.size()), mIndices(points.size())
|
||||
{
|
||||
// compute the AABB of the input
|
||||
mPoints[0] = points[0];
|
||||
mAABB.Set(mPoints[0]);
|
||||
for (unsigned int i=1 ; i<mPoints.size() ; ++i)
|
||||
{
|
||||
mPoints[i] = points[i];
|
||||
mIndices[i] = i;
|
||||
mAABB.Add(mPoints[i]);
|
||||
}
|
||||
|
||||
mNodes.reserve(4*mPoints.size()/nofPointsPerCell);
|
||||
mNodes.reserve(4*mPoints.size()/nofPointsPerCell);
|
||||
|
||||
//first node inserted (no leaf). The others are made by the createTree function (recursively)
|
||||
mNodes.resize(1);
|
||||
mNodes.back().leaf = 0;
|
||||
createTree(0, 0, mPoints.size(), 1, nofPointsPerCell, maxDepth);
|
||||
}
|
||||
//first node inserted (no leaf). The others are made by the createTree function (recursively)
|
||||
mNodes.resize(1);
|
||||
mNodes.back().leaf = 0;
|
||||
createTree(0, 0, mPoints.size(), 1, nofPointsPerCell, maxDepth);
|
||||
}
|
||||
|
||||
template<typename Scalar>
|
||||
KdTree<Scalar>::~KdTree()
|
||||
{
|
||||
}
|
||||
template<typename Scalar>
|
||||
KdTree<Scalar>::~KdTree()
|
||||
{
|
||||
}
|
||||
|
||||
|
||||
/** Performs the kNN query.
|
||||
*
|
||||
* This algorithm uses the simple distance to the split plane to prune nodes.
|
||||
* A more elaborated approach consists to track the closest corner of the cell
|
||||
* relatively to the current query point. This strategy allows to save about 5%
|
||||
* of the leaves. However, in practice the slight overhead due to this tracking
|
||||
* reduces the overall performance.
|
||||
*
|
||||
* This algorithm also use a simple stack while a priority queue using the squared
|
||||
* distances to the cells as a priority values allows to save about 10% of the leaves.
|
||||
* But, again, priority queue insertions and deletions are quite involved, and therefore
|
||||
* a simple stack is by far much faster.
|
||||
*
|
||||
* The result of the query, the k-nearest neighbors, are stored into the stack mNeighborQueue, where the
|
||||
* topmost element [0] is NOT the nearest but the farthest!! (they are not sorted but arranged into a heap).
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryK(const VectorType& queryPoint, int k, PriorityQueue& mNeighborQueue)
|
||||
{
|
||||
mNeighborQueue.setMaxSize(k);
|
||||
mNeighborQueue.init();
|
||||
mNeighborQueue.insert(0xffffffff, std::numeric_limits<Scalar>::max());
|
||||
/** Performs the kNN query.
|
||||
*
|
||||
* This algorithm uses the simple distance to the split plane to prune nodes.
|
||||
* A more elaborated approach consists to track the closest corner of the cell
|
||||
* relatively to the current query point. This strategy allows to save about 5%
|
||||
* of the leaves. However, in practice the slight overhead due to this tracking
|
||||
* reduces the overall performance.
|
||||
*
|
||||
* This algorithm also use a simple stack while a priority queue using the squared
|
||||
* distances to the cells as a priority values allows to save about 10% of the leaves.
|
||||
* But, again, priority queue insertions and deletions are quite involved, and therefore
|
||||
* a simple stack is by far much faster.
|
||||
*
|
||||
* The result of the query, the k-nearest neighbors, are stored into the stack mNeighborQueue, where the
|
||||
* topmost element [0] is NOT the nearest but the farthest!! (they are not sorted but arranged into a heap).
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryK(const VectorType& queryPoint, int k, PriorityQueue& mNeighborQueue)
|
||||
{
|
||||
mNeighborQueue.setMaxSize(k);
|
||||
mNeighborQueue.init();
|
||||
mNeighborQueue.insert(0xffffffff, std::numeric_limits<Scalar>::max());
|
||||
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
|
||||
while (count)
|
||||
{
|
||||
//we select the last node (AABB) inserted in the stack
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
while (count)
|
||||
{
|
||||
//we select the last node (AABB) inserted in the stack
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
|
||||
//while going down the tree qnode.nodeId is the nearest sub-tree, otherwise,
|
||||
//in backtracking, qnode.nodeId is the other sub-tree that will be visited iff
|
||||
//the actual nearest node is further than the split distance.
|
||||
Node& node = mNodes[qnode.nodeId];
|
||||
//while going down the tree qnode.nodeId is the nearest sub-tree, otherwise,
|
||||
//in backtracking, qnode.nodeId is the other sub-tree that will be visited iff
|
||||
//the actual nearest node is further than the split distance.
|
||||
Node& node = mNodes[qnode.nodeId];
|
||||
|
||||
//if the distance is less than the top of the max-heap, it could be one of the k-nearest neighbours
|
||||
if (qnode.sq < mNeighborQueue.getTopWeight())
|
||||
{
|
||||
//when we arrive to a lef
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; //pop of the leaf
|
||||
//if the distance is less than the top of the max-heap, it could be one of the k-nearest neighbours
|
||||
if (qnode.sq < mNeighborQueue.getTopWeight())
|
||||
{
|
||||
//when we arrive to a lef
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; //pop of the leaf
|
||||
|
||||
//end is the index of the last element of the leaf in mPoints
|
||||
unsigned int end = node.start+node.size;
|
||||
//adding the element of the leaf to the heap
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
mNeighborQueue.insert(mIndices[i], vcg::SquaredNorm(queryPoint - mPoints[i]));
|
||||
}
|
||||
//otherwise, if we're not on a leaf
|
||||
else
|
||||
{
|
||||
// the new offset is the distance between the searched point and the actual split coordinate
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
//end is the index of the last element of the leaf in mPoints
|
||||
unsigned int end = node.start+node.size;
|
||||
//adding the element of the leaf to the heap
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
mNeighborQueue.insert(mIndices[i], vcg::SquaredNorm(queryPoint - mPoints[i]));
|
||||
}
|
||||
//otherwise, if we're not on a leaf
|
||||
else
|
||||
{
|
||||
// the new offset is the distance between the searched point and the actual split coordinate
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
|
||||
//left sub-tree
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
//in the father's nodeId we save the index of the other sub-tree (for backtracking)
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
//right sub-tree (same as above)
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
//distance is inherited from the father (while descending the tree it's equal to 0)
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
//distance of the father is the squared distance from the split plane
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
}
|
||||
//left sub-tree
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
//in the father's nodeId we save the index of the other sub-tree (for backtracking)
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
//right sub-tree (same as above)
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
//distance is inherited from the father (while descending the tree it's equal to 0)
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
//distance of the father is the squared distance from the split plane
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/** Performs the distance query.
|
||||
*
|
||||
* The result of the query, all the points within the distance dist form the query point, is the vector of the indeces
|
||||
* and the vector of the squared distances from the query point.
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryDist(const VectorType& queryPoint, float dist, std::vector<unsigned int>& points, std::vector<Scalar>& sqrareDists)
|
||||
{
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
/** Performs the distance query.
|
||||
*
|
||||
* The result of the query, all the points within the distance dist form the query point, is the vector of the indeces
|
||||
* and the vector of the squared distances from the query point.
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryDist(const VectorType& queryPoint, float dist, std::vector<unsigned int>& points, std::vector<Scalar>& sqrareDists)
|
||||
{
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
|
||||
float sqrareDist = dist*dist;
|
||||
while (count)
|
||||
{
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
Node & node = mNodes[qnode.nodeId];
|
||||
float sqrareDist = dist*dist;
|
||||
while (count)
|
||||
{
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
Node & node = mNodes[qnode.nodeId];
|
||||
|
||||
if (qnode.sq < sqrareDist)
|
||||
{
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; // pop
|
||||
unsigned int end = node.start+node.size;
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
{
|
||||
float pointSquareDist = vcg::SquaredNorm(queryPoint - mPoints[i]);
|
||||
if (pointSquareDist < sqrareDist)
|
||||
{
|
||||
points.push_back(mIndices[i]);
|
||||
sqrareDists.push_back(pointSquareDist);
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// replace the stack top by the farthest and push the closest
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (qnode.sq < sqrareDist)
|
||||
{
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; // pop
|
||||
unsigned int end = node.start+node.size;
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
{
|
||||
float pointSquareDist = vcg::SquaredNorm(queryPoint - mPoints[i]);
|
||||
if (pointSquareDist < sqrareDist)
|
||||
{
|
||||
points.push_back(mIndices[i]);
|
||||
sqrareDists.push_back(pointSquareDist);
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// replace the stack top by the farthest and push the closest
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/** Searchs the closest point.
|
||||
*
|
||||
* The result of the query, the closest point to the query point, is the index of the point and
|
||||
* and the squared distance from the query point.
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryClosest(const VectorType& queryPoint, unsigned int& index, Scalar& dist)
|
||||
{
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
/** Searchs the closest point.
|
||||
*
|
||||
* The result of the query, the closest point to the query point, is the index of the point and
|
||||
* and the squared distance from the query point.
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::doQueryClosest(const VectorType& queryPoint, unsigned int& index, Scalar& dist)
|
||||
{
|
||||
QueryNode mNodeStack[64];
|
||||
mNodeStack[0].nodeId = 0;
|
||||
mNodeStack[0].sq = 0.f;
|
||||
unsigned int count = 1;
|
||||
|
||||
int minIndex = mIndices.size() / 2;
|
||||
Scalar minDist = vcg::SquaredNorm(queryPoint - mPoints[minIndex]);
|
||||
minIndex = mIndices[minIndex];
|
||||
int minIndex = mIndices.size() / 2;
|
||||
Scalar minDist = vcg::SquaredNorm(queryPoint - mPoints[minIndex]);
|
||||
minIndex = mIndices[minIndex];
|
||||
|
||||
while (count)
|
||||
{
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
Node & node = mNodes[qnode.nodeId];
|
||||
while (count)
|
||||
{
|
||||
QueryNode& qnode = mNodeStack[count-1];
|
||||
Node & node = mNodes[qnode.nodeId];
|
||||
|
||||
if (qnode.sq < minDist)
|
||||
{
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; // pop
|
||||
unsigned int end = node.start+node.size;
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
{
|
||||
float pointSquareDist = vcg::SquaredNorm(queryPoint - mPoints[i]);
|
||||
if (pointSquareDist < minDist)
|
||||
{
|
||||
minDist = pointSquareDist;
|
||||
minIndex = mIndices[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// replace the stack top by the farthest and push the closest
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
index = minIndex;
|
||||
dist = minDist;
|
||||
}
|
||||
if (qnode.sq < minDist)
|
||||
{
|
||||
if (node.leaf)
|
||||
{
|
||||
--count; // pop
|
||||
unsigned int end = node.start+node.size;
|
||||
for (unsigned int i=node.start ; i<end ; ++i)
|
||||
{
|
||||
float pointSquareDist = vcg::SquaredNorm(queryPoint - mPoints[i]);
|
||||
if (pointSquareDist < minDist)
|
||||
{
|
||||
minDist = pointSquareDist;
|
||||
minIndex = mIndices[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// replace the stack top by the farthest and push the closest
|
||||
float new_off = queryPoint[node.dim] - node.splitValue;
|
||||
if (new_off < 0.)
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId;
|
||||
qnode.nodeId = node.firstChildId+1;
|
||||
}
|
||||
else
|
||||
{
|
||||
mNodeStack[count].nodeId = node.firstChildId+1;
|
||||
qnode.nodeId = node.firstChildId;
|
||||
}
|
||||
mNodeStack[count].sq = qnode.sq;
|
||||
qnode.sq = new_off*new_off;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// pop
|
||||
--count;
|
||||
}
|
||||
}
|
||||
index = minIndex;
|
||||
dist = minDist;
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* Split the subarray between start and end in two part, one with the elements less than splitValue,
|
||||
* the other with the elements greater or equal than splitValue. The elements are compared
|
||||
* using the "dim" coordinate [0 = x, 1 = y, 2 = z].
|
||||
*/
|
||||
template<typename Scalar>
|
||||
unsigned int KdTree<Scalar>::split(int start, int end, unsigned int dim, float splitValue)
|
||||
{
|
||||
int l(start), r(end-1);
|
||||
for ( ; l<r ; ++l, --r)
|
||||
{
|
||||
while (l < end && mPoints[l][dim] < splitValue)
|
||||
l++;
|
||||
while (r >= start && mPoints[r][dim] >= splitValue)
|
||||
r--;
|
||||
if (l > r)
|
||||
break;
|
||||
std::swap(mPoints[l],mPoints[r]);
|
||||
std::swap(mIndices[l],mIndices[r]);
|
||||
}
|
||||
//returns the index of the first element on the second part
|
||||
return (mPoints[l][dim] < splitValue ? l+1 : l);
|
||||
}
|
||||
/**
|
||||
* Split the subarray between start and end in two part, one with the elements less than splitValue,
|
||||
* the other with the elements greater or equal than splitValue. The elements are compared
|
||||
* using the "dim" coordinate [0 = x, 1 = y, 2 = z].
|
||||
*/
|
||||
template<typename Scalar>
|
||||
unsigned int KdTree<Scalar>::split(int start, int end, unsigned int dim, float splitValue)
|
||||
{
|
||||
int l(start), r(end-1);
|
||||
for ( ; l<r ; ++l, --r)
|
||||
{
|
||||
while (l < end && mPoints[l][dim] < splitValue)
|
||||
l++;
|
||||
while (r >= start && mPoints[r][dim] >= splitValue)
|
||||
r--;
|
||||
if (l > r)
|
||||
break;
|
||||
std::swap(mPoints[l],mPoints[r]);
|
||||
std::swap(mIndices[l],mIndices[r]);
|
||||
}
|
||||
//returns the index of the first element on the second part
|
||||
return (mPoints[l][dim] < splitValue ? l+1 : l);
|
||||
}
|
||||
|
||||
/** recursively builds the kdtree
|
||||
*
|
||||
* The heuristic is the following:
|
||||
* - if the number of points in the node is lower than targetCellsize then make a leaf
|
||||
* - else compute the AABB of the points of the node and split it at the middle of
|
||||
* the largest AABB dimension.
|
||||
*
|
||||
* This strategy might look not optimal because it does not explicitly prune empty space,
|
||||
* unlike more advanced SAH-like techniques used for RT. On the other hand it leads to a shorter tree,
|
||||
* faster to traverse and our experience shown that in the special case of kNN queries,
|
||||
* this strategy is indeed more efficient (and much faster to build). Moreover, for volume data
|
||||
* (e.g., fluid simulation) pruning the empty space is useless.
|
||||
*
|
||||
* Actually, storing at each node the exact AABB (we therefore have a binary BVH) allows
|
||||
* to prune only about 10% of the leaves, but the overhead of this pruning (ball/ABBB intersection)
|
||||
* is more expensive than the gain it provides and the memory consumption is x4 higher !
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::createTree(unsigned int nodeId, unsigned int start, unsigned int end, unsigned int level, unsigned int targetCellSize, unsigned int targetMaxDepth)
|
||||
{
|
||||
//select the first node
|
||||
Node& node = mNodes[nodeId];
|
||||
AxisAlignedBoxType aabb;
|
||||
/** recursively builds the kdtree
|
||||
*
|
||||
* The heuristic is the following:
|
||||
* - if the number of points in the node is lower than targetCellsize then make a leaf
|
||||
* - else compute the AABB of the points of the node and split it at the middle of
|
||||
* the largest AABB dimension.
|
||||
*
|
||||
* This strategy might look not optimal because it does not explicitly prune empty space,
|
||||
* unlike more advanced SAH-like techniques used for RT. On the other hand it leads to a shorter tree,
|
||||
* faster to traverse and our experience shown that in the special case of kNN queries,
|
||||
* this strategy is indeed more efficient (and much faster to build). Moreover, for volume data
|
||||
* (e.g., fluid simulation) pruning the empty space is useless.
|
||||
*
|
||||
* Actually, storing at each node the exact AABB (we therefore have a binary BVH) allows
|
||||
* to prune only about 10% of the leaves, but the overhead of this pruning (ball/ABBB intersection)
|
||||
* is more expensive than the gain it provides and the memory consumption is x4 higher !
|
||||
*/
|
||||
template<typename Scalar>
|
||||
void KdTree<Scalar>::createTree(unsigned int nodeId, unsigned int start, unsigned int end, unsigned int level, unsigned int targetCellSize, unsigned int targetMaxDepth)
|
||||
{
|
||||
//select the first node
|
||||
Node& node = mNodes[nodeId];
|
||||
AxisAlignedBoxType aabb;
|
||||
|
||||
//putting all the points in the bounding box
|
||||
aabb.Set(mPoints[start]);
|
||||
for (unsigned int i=start+1 ; i<end ; ++i)
|
||||
aabb.Add(mPoints[i]);
|
||||
//putting all the points in the bounding box
|
||||
aabb.Set(mPoints[start]);
|
||||
for (unsigned int i=start+1 ; i<end ; ++i)
|
||||
aabb.Add(mPoints[i]);
|
||||
|
||||
//bounding box diagonal
|
||||
VectorType diag = aabb.max - aabb.min;
|
||||
//bounding box diagonal
|
||||
VectorType diag = aabb.max - aabb.min;
|
||||
|
||||
//the split "dim" is the dimension of the box with the biggest value
|
||||
unsigned int dim;
|
||||
if (diag.X() > diag.Y())
|
||||
dim = diag.X() > diag.Z() ? 0 : 2;
|
||||
else
|
||||
dim = diag.Y() > diag.Z() ? 1 : 2;
|
||||
|
||||
node.dim = dim;
|
||||
//we divide the bounding box in 2 partitions, considering the average of the "dim" dimension
|
||||
node.splitValue = Scalar(0.5*(aabb.max[dim] + aabb.min[dim]));
|
||||
//the split "dim" is the dimension of the box with the biggest value
|
||||
unsigned int dim;
|
||||
if (diag.X() > diag.Y())
|
||||
dim = diag.X() > diag.Z() ? 0 : 2;
|
||||
else
|
||||
dim = diag.Y() > diag.Z() ? 1 : 2;
|
||||
|
||||
//midId is the index of the first element in the second partition
|
||||
unsigned int midId = split(start, end, dim, node.splitValue);
|
||||
node.dim = dim;
|
||||
//we divide the bounding box in 2 partitions, considering the average of the "dim" dimension
|
||||
node.splitValue = Scalar(0.5*(aabb.max[dim] + aabb.min[dim]));
|
||||
|
||||
//midId is the index of the first element in the second partition
|
||||
unsigned int midId = split(start, end, dim, node.splitValue);
|
||||
|
||||
|
||||
node.firstChildId = mNodes.size();
|
||||
mNodes.resize(mNodes.size()+2);
|
||||
node.firstChildId = mNodes.size();
|
||||
mNodes.resize(mNodes.size()+2);
|
||||
|
||||
{
|
||||
// left child
|
||||
unsigned int childId = mNodes[nodeId].firstChildId;
|
||||
Node& child = mNodes[childId];
|
||||
if (midId - start <= targetCellSize || level>=targetMaxDepth)
|
||||
{
|
||||
child.leaf = 1;
|
||||
child.start = start;
|
||||
child.size = midId - start;
|
||||
}
|
||||
else
|
||||
{
|
||||
child.leaf = 0;
|
||||
createTree(childId, start, midId, level+1, targetCellSize, targetMaxDepth);
|
||||
}
|
||||
}
|
||||
{
|
||||
// left child
|
||||
unsigned int childId = mNodes[nodeId].firstChildId;
|
||||
Node& child = mNodes[childId];
|
||||
if (midId - start <= targetCellSize || level>=targetMaxDepth)
|
||||
{
|
||||
child.leaf = 1;
|
||||
child.start = start;
|
||||
child.size = midId - start;
|
||||
}
|
||||
else
|
||||
{
|
||||
child.leaf = 0;
|
||||
createTree(childId, start, midId, level+1, targetCellSize, targetMaxDepth);
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
// right child
|
||||
unsigned int childId = mNodes[nodeId].firstChildId+1;
|
||||
Node& child = mNodes[childId];
|
||||
if (end - midId <= targetCellSize || level>=targetMaxDepth)
|
||||
{
|
||||
child.leaf = 1;
|
||||
child.start = midId;
|
||||
child.size = end - midId;
|
||||
}
|
||||
else
|
||||
{
|
||||
child.leaf = 0;
|
||||
createTree(childId, midId, end, level+1, targetCellSize, targetMaxDepth);
|
||||
}
|
||||
}
|
||||
}
|
||||
{
|
||||
// right child
|
||||
unsigned int childId = mNodes[nodeId].firstChildId+1;
|
||||
Node& child = mNodes[childId];
|
||||
if (end - midId <= targetCellSize || level>=targetMaxDepth)
|
||||
{
|
||||
child.leaf = 1;
|
||||
child.start = midId;
|
||||
child.size = end - midId;
|
||||
}
|
||||
else
|
||||
{
|
||||
child.leaf = 0;
|
||||
createTree(childId, midId, end, level+1, targetCellSize, targetMaxDepth);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
#endif
|
||||
|
|
|
|||
Loading…
Reference in New Issue