dynamic_pointcloud_example.cpp

/***********************************************************************
 * 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<num_t> cloud;

    // construct a kd-tree index:
    using my_kd_tree_t = nanoflann::KDTreeSingleIndexDynamicAdaptor<
        nanoflann::L2_Simple_Adaptor<num_t, PointCloud<num_t>>,
        PointCloud<num_t>, 3 /* dim */
        >;

    dump_mem_usage();

    my_kd_tree_t index(3 /*dim*/, cloud, {10 /* max leaf */});

    // Generate points:
    generateRandomPointCloud(cloud, N);

    num_t query_pt[3] = {0.5, 0.5, 0.5};

    // add points in chunks at a time
    size_t chunk_size = 100;
    for (size_t i = 0; i < N; i = i + chunk_size)
    {
        size_t end = std::min<size_t>(i + chunk_size, N - 1);
        // Inserts all points from [i, end]
        index.addPoints(i, end);
    }

    // remove a point
    size_t removePointIndex = N - 1;
    index.removePoint(removePointIndex);

    dump_mem_usage();
    {
        std::cout << "Searching for 1 element..." << std::endl;
        // 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, {10});

        std::cout << "knnSearch(nn=" << num_results << "): \n";
        std::cout << "ret_index=" << ret_index
                  << " out_dist_sqr=" << out_dist_sqr << std::endl;
        std::cout << "point: ("
                  << "point: (" << cloud.pts[ret_index].x << ", "
                  << cloud.pts[ret_index].y << ", " << cloud.pts[ret_index].z
                  << ")" << std::endl;
        std::cout << std::endl;
    }
    {
        // do a knn search searching for more than one result
        const size_t num_results = 5;
        std::cout << "Searching for " << num_results << " elements"
                  << std::endl;
        size_t                         ret_index[num_results];
        num_t                          out_dist_sqr[num_results];
        nanoflann::KNNResultSet<num_t> resultSet(num_results);
        resultSet.init(ret_index, out_dist_sqr);
        index.findNeighbors(resultSet, query_pt);

        std::cout << "knnSearch(nn=" << num_results << "): \n";
        std::cout << "Results: " << std::endl;
        for (size_t i = 0; i < resultSet.size(); ++i)
        {
            std::cout << "#" << i << ",\t"
                      << "index: " << ret_index[i] << ",\t"
                      << "dist: " << out_dist_sqr[i] << ",\t"
                      << "point: (" << cloud.pts[ret_index[i]].x << ", "
                      << cloud.pts[ret_index[i]].y << ", "
                      << cloud.pts[ret_index[i]].z << ")" << std::endl;
        }
        std::cout << std::endl;
    }
    {
        // Unsorted radius search:
        std::cout << "Unsorted radius search" << std::endl;
        const num_t                                       radiusSqr = 1;
        std::vector<nanoflann::ResultItem<size_t, num_t>> indices_dists;
        nanoflann::RadiusResultSet<num_t, size_t>         resultSet(
                    radiusSqr, indices_dists);

        index.findNeighbors(resultSet, query_pt);

        nanoflann::ResultItem<size_t, num_t> worst_pair =
            resultSet.worst_item();
        std::cout << "Worst pair: idx=" << worst_pair.first
                  << " dist=" << worst_pair.second << std::endl;
        std::cout << "point: (" << cloud.pts[worst_pair.first].x << ", "
                  << cloud.pts[worst_pair.first].y << ", "
                  << cloud.pts[worst_pair.first].z << ")" << std::endl;
        std::cout << std::endl;
    }
}

int main()
{
    // Randomize Seed
    srand(static_cast<unsigned int>(time(nullptr)));
    kdtree_demo<float>(1000000);
    kdtree_demo<double>(1000000);
    return 0;
}