auto.hpp

/*
* SPDX-License-Identifier: BSD-3-Clause
*
*  Point Cloud Library (PCL) - www.pointclouds.org
*  Copyright (c) 2025-, Open Perception Inc.
*
*  All rights reserved
*/
 
#ifndef PCL_SEARCH_AUTO_IMPL_HPP_
#define PCL_SEARCH_AUTO_IMPL_HPP_
 
#include <pcl/pcl_config.h>
#include <pcl/common/utils.h> // for ignore
#include <pcl/search/auto.h>
#include <pcl/search/brute_force.h>
#if PCL_HAS_FLANN
#include <pcl/search/kdtree.h>
#endif
#include <pcl/search/kdtree_nanoflann.h>
#include <pcl/search/octree.h>
#include <pcl/search/organized.h>
 
template<typename PointT>
pcl::search::Search<PointT> * pcl::search::autoSelectMethod(const typename pcl::PointCloud<PointT>::ConstPtr& cloud, const pcl::IndicesConstPtr& indices, bool sorted_results, pcl::search::Purpose purpose) {
  pcl::search::Search<PointT> * searcher = nullptr;
  if constexpr (pcl::traits::has_xyz_v<PointT>) {
    if (cloud->isOrganized ()) {
      searcher = new pcl::search::OrganizedNeighbor<PointT> (sorted_results);
      if(searcher->setInputCloud (cloud, indices)) { // may return false if OrganizedNeighbor cannot work with the cloud, then use another search method instead
        return searcher;
      }
      delete searcher;
    }
  }
 
#if PCL_HAS_NANOFLANN
  // we get the number of search dimensions as a compile-time-constant via NR_DIMS. NR_DIMS may be -1 if it is not possible to determine the dimensions at compile-time (only at run-time), however then searching may be slower. If NR_DIMS is not -1, it must be the same as the return value of getNumberOfDimensions().
  searcher = new pcl::search::KdTreeNanoflann<PointT, pcl::DefaultPointRepresentation<PointT>::NR_DIMS> (sorted_results, (purpose == pcl::search::Purpose::one_knn_search ? 10 : 20));
  if(searcher->setInputCloud (cloud, indices)) {
    return searcher;
  }
  delete searcher;
#else
  pcl::utils::ignore(purpose);
#endif
 
#if PCL_HAS_FLANN
  searcher = new pcl::search::KdTree<PointT> (sorted_results);
  if(searcher->setInputCloud (cloud, indices)) {
    return searcher;
  }
  delete searcher;
#endif
 
  if constexpr (pcl::traits::has_xyz_v<PointT>) {
    searcher = new pcl::search::Octree<PointT> (0.01); // TODO a better heuristic to choose octree resolution?
    searcher->setSortedResults (sorted_results);
    if(searcher->setInputCloud (cloud, indices)) {
      return searcher;
    }
    delete searcher;
  }
 
  searcher = new pcl::search::BruteForce<PointT> (sorted_results);
  searcher->setInputCloud (cloud, indices);
  return searcher;
}
 
template<typename PointT>
pcl::search::Search<PointT> * pcl::search::autoSelectMethod(const typename pcl::PointCloud<PointT>::ConstPtr& cloud, const pcl::IndicesConstPtr& indices, const pcl::shared_ptr<const pcl::PointRepresentation<PointT> >& point_representation, bool sorted_results, pcl::search::Purpose purpose) {
#if PCL_HAS_NANOFLANN
  // we get the number of search dimensions from the given point_representation, but we can only get it a run-time. Often, this is the same as when DefaultPointRepresentation is used, in this case we use the fast option of KdTreeNanoflann with a static number of dimensions (second template parameter). Otherwise, we use a KdTreeNanoflann with a dynamic number of dimensions (-1 as template parameter, slightly slower).
  if(point_representation->getNumberOfDimensions() == pcl::DefaultPointRepresentation<PointT>::NR_DIMS) {
    auto searcher = new pcl::search::KdTreeNanoflann<PointT, pcl::DefaultPointRepresentation<PointT>::NR_DIMS> (sorted_results, (purpose == pcl::search::Purpose::one_knn_search ? 10 : 20));
    searcher->setPointRepresentation (point_representation);
    if(searcher->setInputCloud (cloud, indices)) {
      return searcher;
    }
    delete searcher;
  }
  {
    auto searcher = new pcl::search::KdTreeNanoflann<PointT, -1> (sorted_results, (purpose == pcl::search::Purpose::one_knn_search ? 10 : 20));
    searcher->setPointRepresentation (point_representation);
    if(searcher->setInputCloud (cloud, indices)) {
      return searcher;
    }
    delete searcher;
  }
#else
  pcl::utils::ignore(purpose);
#endif
 
#if PCL_HAS_FLANN
  {
    auto searcher = new pcl::search::KdTree<PointT> (sorted_results);
    searcher->setPointRepresentation (point_representation);
    if(searcher->setInputCloud (cloud, indices)) {
      return searcher;
    }
    delete searcher;
  }
#endif
 
  {
    auto searcher = new pcl::search::BruteForce<PointT> (sorted_results);
    searcher->setPointRepresentation (point_representation);
    searcher->setInputCloud (cloud, indices);
    return searcher;
  }
}
 
#define PCL_INSTANTIATE_AutoSelectMethod(T) template PCL_EXPORTS pcl::search::Search<T> * pcl::search::autoSelectMethod<T>(const typename pcl::PointCloud<T>::ConstPtr& cloud, const pcl::IndicesConstPtr& indices, bool sorted_results, pcl::search::Purpose purpose);
 
#define PCL_INSTANTIATE_AutoSelectMethod2(T) template PCL_EXPORTS pcl::search::Search<T> * pcl::search::autoSelectMethod<T>(const typename pcl::PointCloud<T>::ConstPtr& cloud, const pcl::IndicesConstPtr& indices, const pcl::shared_ptr<const pcl::PointRepresentation<T> >& point_representation, bool sorted_results, pcl::search::Purpose purpose);
 
#endif  //#ifndef PCL_SEARCH_AUTO_IMPL_HPP_