Ponca::KdTree neighbor searches

This is an example of how to use Ponca::KdTree to perform nearest, k-nearest and range neighbor search.

/*
This Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one at http://mozilla.org/MPL/2.0/.
*/
 
#include <iostream>
#include <random>
#include <Ponca/SpatialPartitioning>
#include <Ponca/src/Common/pointTypes.h>
 
#include <Eigen/Core>
 
using DataPoint = Ponca::PointPosition<float, 3>;
 
int main()
{
    // generate N random points
    constexpr int N {100000};
 
 
    std::vector<DataPoint> points(N);
    std::generate(points.begin(), points.end(), [](){
        return DataPoint{100 * DataPoint::VectorType::Random()};
    });
    // build the k-d tree
    Ponca::KdTreeDense<DataPoint> kdtreeDense(points);
 
    int seed = 0;
    std::vector<int> indices(N);
    std::vector<int> sampling(N / 2);
    std::iota(indices.begin(), indices.end(), 0);
    std::sample(indices.begin(), indices.end(), sampling.begin(), N / 2, std::mt19937(seed));
    Ponca::KdTreeSparse<DataPoint> kdtreeSparse(points, sampling);
 
    // Abstract pointer type that can receive KdTreeSparse or KdTreeDense objects
    Ponca::KdTree<DataPoint> *kdtree {nullptr};
 
    // Assign sparse
    kdtree = &kdtreeSparse; // Or kdtree = new Ponca::KdTreeSparse<DataPoint> (points, sampling);
 
    // Assign dense
    kdtree = &kdtreeDense; // Or kdtree = new Ponca::KdTreeDense<DataPoint> (points, sampling);
 
    // neighbor searches are done below from these arbitrary index and point
    const int query_idx = 10;
    const DataPoint::VectorType query_pt{-10.0, 0.5, 75.0};
 
    const int k = 10;
    Ponca::KnnGraph<DataPoint> knnGraph(kdtreeDense, k);
 
    std::cout << "The nearest neighbor of the point at index " << query_idx << " is at index "
              << *kdtree->nearestNeighbor(query_idx).begin() << std::endl;
    std::cout << "The nearest neighbor of the point (" << query_pt.transpose() << ") is at index "
              << *kdtree->nearestNeighbor(query_pt).begin() << std::endl;
 
    std::cout << "The " << k << "-nearest neighbors of the point at index " << query_idx << " are at indices: ";
    for(int neighbor_idx : kdtreeDense.kNearestNeighbors(query_idx, k)) { // Iterates over the neighbors of query_idx
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
 
    std::cout << "The " << k << "-nearest neighbors of the point (" << query_pt.transpose() << ") are at indices: ";
    for(auto neighbor_idx : kdtreeDense.kNearestNeighbors(query_pt, k)) {
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
 
    std::cout << "The nearest neighbors of the point at index " << query_idx << " are at indices: ";
    for(int neighbor_idx : knnGraph.kNearestNeighbors(query_idx)) {
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
 
    const int second_query_idx = 5;
    constexpr DataPoint::Scalar radius = 5.25;
    auto rangeNeighbors = kdtreeDense.rangeNeighbors(query_idx, radius); // First query
 
    std::cout << "The neighbors of the point at index " << second_query_idx << " at a distance " << radius << " are at indices: ";
    for(int neighbor_idx : rangeNeighbors(second_query_idx, radius)) { // Iterate over a new rangeNeighbors query
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
    auto posRangeNeighbors = kdtreeDense.rangeNeighborsQuery(); // Empty query
 
    std::cout << "The neighbors of the point (" << query_pt.transpose() << ") at a distance " << radius << " are at indices: ";
    for(auto neighbor_idx : posRangeNeighbors(query_pt, radius)) {
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
 
    auto knnRangeNeighbors = knnGraph.rangeNeighborsIndexQuery(); // Empty query
 
    std::cout << "The neighbors of the point (" << query_pt.transpose() << ") at a distance " << radius << " are at indices: ";
    for(auto neighbor_idx : knnRangeNeighbors(query_idx, radius)) { // Iterates over the neighbors of query_idx
        std::cout << neighbor_idx << ", ";
    }
    std::cout << std::endl;
 
    return 1;
}