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SO3_adaptor_example.cpp
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/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2011-2025 Jose Luis Blanco (joseluisblancoc@gmail.com).
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*************************************************************************/
#include <cstdlib>
#include <ctime>
#include <iostream>
#include <nanoflann.hpp>
#include "utils.h"
template <typename num_t>
void kdtree_demo(const size_t N)
{
PointCloud_Quat<num_t> cloud;
// Generate points:
generateRandomPointCloud_Quat(cloud, N);
num_t query_pt[4] = {0.5, 0.5, 0.5, 0.5};
// construct a kd-tree index:
using my_kd_tree_t = nanoflann::KDTreeSingleIndexAdaptor<
nanoflann::SO3_Adaptor<num_t, PointCloud_Quat<num_t>>,
PointCloud_Quat<num_t>, 4 /* dim */
>;
dump_mem_usage();
my_kd_tree_t index(4 /*dim*/, cloud, {10 /* max leaf */});
dump_mem_usage();
{
// do a knn search
const size_t num_results = 1;
size_t ret_index;
num_t out_dist_sqr;
nanoflann::KNNResultSet<num_t> resultSet(num_results);
resultSet.init(&ret_index, &out_dist_sqr);
index.findNeighbors(resultSet, &query_pt[0]);
std::cout << "knnSearch(nn=" << num_results << "): \n";
std::cout << "ret_index=" << ret_index
<< " out_dist_sqr=" << out_dist_sqr << std::endl;
}
}
int main()
{
// Randomize Seed
srand(static_cast<unsigned int>(time(nullptr)));
kdtree_demo<float>(1000000);
kdtree_demo<double>(1000000);
return 0;
}