Nanoflann vs CGAL KD-tree implementation of photon ray tracer for speed comparison
#include <CGAL/Timer.h>
/// Nanoflann code
#include <nanoflann.hpp>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <vector>
using namespace std;
using namespace nanoflann;
template <typename T>
struct Point
{
Point(T x, T y, T z)
: x(x), y(y), z(z)
{}
T x,y,z;
};
template <typename T>
ostream& operator <<(ostream& os, const Point<T>& p)
{
os << p.x << " " << p.y << " " << p.z ;
return os;
}
// This is an exampleof a custom data set class
template <typename T>
struct PointCloud
{
std::vector<Point<T> > pts;
// Must return the number of data points
inline size_t kdtree_get_point_count() const { return pts.size(); }
// Returns the distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
inline T kdtree_distance(const T *p1, const size_t idx_p2,size_t /*size*/) const
{
const T d0=p1[0]-pts[idx_p2].x;
const T d1=p1[1]-pts[idx_p2].y;
const T d2=p1[2]-pts[idx_p2].z;
return d0*d0+d1*d1+d2*d2;
}
// Returns the dim'th component of the idx'th point in the class:
// Since this is inlined and the "dim" argument is typically an immediate value, the
// "if/else's" are actually solved at compile time.
inline T kdtree_get_pt(const size_t idx, int dim) const
{
if (dim==0) return pts[idx].x;
else if (dim==1) return pts[idx].y;
else return pts[idx].z;
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
template <class BBOX>
bool kdtree_get_bbox(BBOX &) const { return false; }
};
template <typename T>
void generateRandomPointCloud(PointCloud<T> &point, istream& is)
{
T x, y, z;
while(is >> x >> y >> z){
point.pts.push_back(Point<T>(x,y,z));
}
std::cout << "Read "<< point.pts.size() << " points\n";
}
template <typename num_t>
void run_nanoflann()
{
PointCloud<num_t> cloud;
// Generate points:
std::ifstream input("points.xyz");
generateRandomPointCloud(cloud, input);
std::vector<Point<double> > queries;
std::ifstream queries_stream("queries.xyz");
double x,y,z;
while( queries_stream >> x >> y >> z){
queries.push_back(Point<double>(x,y,z));
}
num_t query_pt[3] = { 0, 0, 0};
CGAL::Timer timer;
timer.start();
// construct a kd-tree index:
typedef KDTreeSingleIndexAdaptor<
L2_Simple_Adaptor<num_t, PointCloud<num_t> > ,
PointCloud<num_t>,
3 /* dim */
> my_kd_tree_t;
my_kd_tree_t index(3 /*dim*/, cloud, KDTreeSingleIndexAdaptorParams(10 /* max leaf */) );
index.buildIndex();
timer.stop();
std::cout << "construction time: " << timer.time() << " sec" << std::endl;
timer.reset();
timer.start();
// do a knn search
const size_t num_results = 50;
size_t ret_index[50];
num_t out_dist_sqr[50];
int size = cloud.pts.size();
std::cout << "start search" << std::endl;
double sum = 0;
for(int i = 0 ; i < size; i++){
query_pt[0] = queries[i].x;
query_pt[1] = queries[i].y;
query_pt[2] = queries[i].z;
nanoflann::KNNResultSet<num_t> resultSet(num_results);
resultSet.init(ret_index, out_dist_sqr );
index.findNeighbors(resultSet, &query_pt[0], nanoflann::SearchParams(10,0));
//std::cout << "knnSearch(nn="<<num_results<<"): \n";
//std::cout << "ret_index=" << ret_index << " out_dist_sqr=" << out_dist_sqr << endl;
//std::cout << cloud.pts[ret_index] << std::endl;
for (std::size_t k=0; k<num_results; ++k)
sum += cloud.pts[ret_index[k]].x;
}
timer.stop();
std::cout << sum << " done in " << timer.time() << " sec."<< std::endl;
}
/// End of Nanoflann code
/// CGAL code
#include <CGAL/Simple_cartesian.h>
#include <CGAL/Orthogonal_k_neighbor_search.h>
#include <CGAL/Search_traits_3.h>
#include <vector>
#include <cmath>
#include <iostream>
#include <fstream>
typedef CGAL::Simple_cartesian<double> K;
typedef K::Point_3 Point_d;
typedef CGAL::Search_traits_3<K> TreeTraits;
typedef CGAL::Orthogonal_k_neighbor_search<TreeTraits> Neighbor_search;
typedef Neighbor_search::Tree Tree;
void run_cgal() {
CGAL::Timer timer;
timer.start();
std::vector<Point_d> points, queries;
Point_d p;
std::ifstream point_stream("points.xyz");
while(point_stream >> p){
points.push_back(p);
}
std::ifstream query_stream("queries.xyz");
while(query_stream >> p ){
queries.push_back(p);
}
timer.stop();
std::cerr << "reading points took " << timer.time() << " sec." << std::endl;
timer.reset();
timer.start();
Tree tree(points.begin(), points.end());
tree.build();
timer.stop();
std::cerr << "tree construction took " << timer.time() << " sec." << std::endl;
// Initialize the search structure, and search all N points
double d = 0;
timer.reset();
timer.start();
for(std::size_t i = 0; i < queries.size(); i++){
Neighbor_search search(tree, queries[i], 50, 0);
// report the N nearest neighbors and their distance
// This should sort all N points by increasing distance from origin
for(Neighbor_search::iterator it = search.begin(); it != search.end(); ++it){
//std::cout << it->first << std::endl;
d += it->first.x();
}
}
timer.stop();
std::cerr << d << std::endl;
std::cerr << queries.size() << " queries in " << timer.time() << " sec." << std::endl;
}
/// End of CGAL code
int main()
{
std::cout << "CGAL\n";
run_cgal();
std::cout << "Nanoflann\n";
run_nanoflann<double>();
}