Move further towards KdTreeNanoflann Part A

apps/src/openni_tracking.cpp

@@ -48,6 +48,7 @@
 #include <pcl/io/openni_grabber.h>
 #include <pcl/sample_consensus/method_types.h>
 #include <pcl/sample_consensus/model_types.h>
+#include <pcl/search/kdtree.h>
 #include <pcl/segmentation/extract_clusters.h>
 #include <pcl/segmentation/extract_polygonal_prism_data.h>
 #include <pcl/segmentation/sac_segmentation.h>

apps/src/pcd_select_object_plane.cpp

@@ -48,6 +48,7 @@
 #include <pcl/geometry/polygon_operations.h>
 #include <pcl/io/pcd_io.h>
 #include <pcl/sample_consensus/sac_model_plane.h> // for pointToPlaneDistance
+#include <pcl/search/kdtree.h>
 #include <pcl/segmentation/edge_aware_plane_comparator.h>
 #include <pcl/segmentation/euclidean_cluster_comparator.h>
 #include <pcl/segmentation/extract_clusters.h>

apps/src/ppf_object_recognition.cpp

@@ -6,6 +6,7 @@
 #include <pcl/registration/ppf_registration.h>
 #include <pcl/sample_consensus/method_types.h>
 #include <pcl/sample_consensus/model_types.h>
+#include <pcl/search/kdtree.h>
 #include <pcl/segmentation/sac_segmentation.h>
 #include <pcl/visualization/pcl_visualizer.h>
 

doc/tutorials/content/sources/conditional_euclidean_clustering/conditional_euclidean_clustering.cpp

@@ -1,7 +1,7 @@
 #include <pcl/point_types.h>
 #include <pcl/io/pcd_io.h>
 #include <pcl/console/time.h>
-
+#include <pcl/search/kdtree.h>
 #include <pcl/filters/voxel_grid.h>
 #include <pcl/features/normal_3d.h>
 #include <pcl/segmentation/conditional_euclidean_clustering.h>

doc/tutorials/content/sources/correspondence_grouping/correspondence_grouping.cpp

@@ -12,7 +12,7 @@
 #include <pcl/kdtree/impl/kdtree_flann.hpp>
 #include <pcl/common/transforms.h>
 #include <pcl/console/parse.h>
-
+#include <pcl/search/kdtree.h>
 typedef pcl::PointXYZRGBA PointType;
 typedef pcl::Normal NormalType;
 typedef pcl::ReferenceFrame RFType;

doc/tutorials/content/sources/cylinder_segmentation/cylinder_segmentation.cpp

@@ -7,7 +7,7 @@
 #include <pcl/sample_consensus/method_types.h>
 #include <pcl/sample_consensus/model_types.h>
 #include <pcl/segmentation/sac_segmentation.h>
-
+#include <pcl/search/kdtree.h>
 typedef pcl::PointXYZ PointT;
 
 int

doc/tutorials/content/sources/pairwise_incremental_registration/pairwise_incremental_registration.cpp

@@ -53,7 +53,7 @@
 #include <pcl/registration/icp.h>
 #include <pcl/registration/icp_nl.h>
 #include <pcl/common/transforms.h>
-
+#include <pcl/search/kdtree.h>
 #include <pcl/visualization/pcl_visualizer.h>
 
 using pcl::visualization::PointCloudColorHandlerGenericField;

doc/tutorials/content/sources/pcl_visualizer/pcl_visualizer_demo.cpp

@@ -8,7 +8,7 @@
 #include <pcl/io/pcd_io.h>
 #include <pcl/visualization/pcl_visualizer.h>
 #include <pcl/console/parse.h>
-
+#include <pcl/search/kdtree.h>
 using namespace std::chrono_literals;
 
 // --------------

doc/tutorials/content/sources/rops_feature/rops_feature.cpp

@@ -1,6 +1,6 @@
 #include <pcl/features/rops_estimation.h>
 #include <pcl/io/pcd_io.h>
-
+#include <pcl/search/kdtree.h>
 int main (int argc, char** argv)
 {
   if (argc != 4)

doc/tutorials/content/sources/template_alignment/template_alignment.cpp

@@ -7,7 +7,7 @@
 #include <pcl/point_types.h>
 #include <pcl/point_cloud.h>
 #include <pcl/io/pcd_io.h>
-#include <pcl/kdtree/kdtree_flann.h>
+#include <pcl/search/kdtree.h>
 #include <pcl/filters/passthrough.h>
 #include <pcl/filters/voxel_grid.h>
 #include <pcl/features/normal_3d.h>

examples/features/example_difference_of_normals.cpp

@@ -14,6 +14,7 @@
 #include <pcl/segmentation/extract_clusters.h>
 #include <pcl/filters/voxel_grid.h>
 #include <pcl/search/organized.h> // for OrganizedNeighbor
+#include <pcl/search/kdtree.h>
 #include <pcl/point_types.h>
 
 #include <pcl/features/don.h>

examples/segmentation/example_extract_clusters_normals.cpp

@@ -42,6 +42,7 @@
 
 #include <pcl/io/pcd_io.h>
 #include <pcl/features/normal_3d.h>
+#include <pcl/search/auto.h>
 #include <pcl/segmentation/extract_clusters.h>
 #define PCL_NO_PRECOMPILE // ConditionalEuclideanClustering is not always instantiated with PointNormal
 #include <pcl/segmentation/conditional_euclidean_clustering.h>
@@ -74,9 +75,8 @@ main (int argc, char **argv)
   ne.compute (*cloud_normals);
   std::cout << "Estimated the normals" << std::endl;
 
-  // Creating the kdtree object for the search method of the extraction
-  pcl::KdTree<pcl::PointXYZ>::Ptr tree_ec  (new pcl::KdTreeFLANN<pcl::PointXYZ> ());
-  tree_ec->setInputCloud (cloud_ptr);
+  // Creating the search method of the extraction
+  pcl::search::Search<pcl::PointXYZ>::Ptr tree_ec(pcl::search::autoSelectMethod<pcl::PointXYZ>(cloud_ptr, false));
 
   // Extracting Euclidean clusters using cloud and its normals
   std::vector<pcl::PointIndices> cluster_indices;

registration/include/pcl/registration/gicp6d.h

@@ -39,7 +39,7 @@
 #pragma once
 
 #include <pcl/registration/gicp.h>
-#include <pcl/search/kdtree.h>
+#include <pcl/search/search.h>
 #include <pcl/memory.h>
 #include <pcl/pcl_exports.h> // for PCL_EXPORTS
 #include <pcl/point_cloud.h>
@@ -118,7 +118,7 @@ class PCL_EXPORTS GeneralizedIterativeClosestPoint6D
   pcl::PointCloud<PointXYZLAB>::Ptr target_lab_;
 
   /** \brief 6d-tree to search in model cloud. */
-  pcl::search::KdTree<PointXYZLAB>::Ptr target_tree_lab_;
+  pcl::search::Search<PointXYZLAB>::Ptr target_tree_lab_;
 
   /** \brief The color weight. */
   float lab_weight_;

registration/src/gicp6d.cpp

@@ -38,8 +38,12 @@
 
 #include <pcl/registration/gicp6d.h>
 #include <pcl/search/kdtree_nanoflann.h>
-#include <pcl/memory.h>                 // for pcl::make_shared
+#include <pcl/memory.h> // for pcl::make_shared
+#include <pcl/pcl_config.h>
 #include <pcl/point_types_conversion.h> // for PointXYZRGBtoXYZLAB
+#if PCL_HAS_FLANN
+#include <pcl/search/kdtree.h>
+#endif
 
 namespace pcl {
 
@@ -84,17 +88,17 @@ GeneralizedIterativeClosestPoint6D::setInputTarget(
 #if PCL_HAS_NANOFLANN
   // 6 search dimensions for MyPointRepresentation; max. 10 points per leaf is a good
   // choice for one-nearest-neighbor search
-  target_tree_lab_.reset(
-      new pcl::search::KdTreeNanoflann<pcl::PointXYZLAB, 6>(false, 10));
-  target_tree_lab_->setPointRepresentation(
-      pcl::make_shared<MyPointRepresentation>(point_rep_));
-  target_tree_lab_->setInputCloud(target_lab_);
+  auto tree =
+      pcl::make_shared<pcl::search::KdTreeNanoflann<pcl::PointXYZLAB, 6>>(false, 10);
+  tree->setPointRepresentation(pcl::make_shared<MyPointRepresentation>(point_rep_));
+  tree->setInputCloud(target_lab_);
+  target_tree_lab_ = tree;
 #else
 #if PCL_HAS_FLANN
-  target_tree_lab_.reset(new pcl::search::KdTree<pcl::PointXYZLAB>(false));
-  target_tree_lab_->setPointRepresentation(
-      pcl::make_shared<MyPointRepresentation>(point_rep_));
-  target_tree_lab_->setInputCloud(target_lab_);
+  auto tree = pcl::make_shared<pcl::search::KdTree<pcl::PointXYZLAB>>(false);
+  tree->setPointRepresentation(pcl::make_shared<MyPointRepresentation>(point_rep_));
+  tree->setInputCloud(target_lab_);
+  target_tree_lab_ = tree;
 #else
 #error "GeneralizedIterativeClosestPoint6D needs nanoflann or FLANN!"
 #endif