allows to create a kdtree from an array of 3D points

easy3d/kdtree/kdtree_search.cpp

@@ -34,4 +34,9 @@ namespace easy3d {
         (void)cloud;
     }
 
+    KdTreeSearch::KdTreeSearch(const std::vector<vec3>& points)
+    {
+        (void)points;
+    }
+
 } // namespace easy3d

easy3d/kdtree/kdtree_search.h

@@ -85,6 +85,12 @@ namespace easy3d {
          */
         explicit KdTreeSearch(const PointCloud *cloud);
 
+        /**
+         * \brief Constructor.
+         * \param points The points for which a KdTree will be constructed.
+         */
+        explicit KdTreeSearch(const std::vector<vec3>& points);
+
         virtual ~KdTreeSearch() = default;
 
         /// \name Closest point query

easy3d/kdtree/kdtree_search_ann.cpp

@@ -69,6 +69,30 @@ namespace easy3d {
     }
 
 
+    KdTreeSearch_ANN::KdTreeSearch_ANN(const std::vector<vec3>& points) : KdTreeSearch(points) {
+        k_for_radius_search_ = 32;
+        LOG(INFO) << "KdTreeSearch_ANN: k = 32 for radius search";
+
+        // prepare data
+        points_num_ = int(points.size());
+#if COPY_POINT_CLOUD // make a copy of the point cloud when constructing the kd-tree
+        points_ = annAllocPts(points_num_, 3);
+        for (int i = 0; i < points_num_; ++i) {
+            const vec3& p = points[i];
+            points_[i][0] = p[0];
+            points_[i][1] = p[1];
+            points_[i][2] = p[2];
+        }
+#else
+        points_ = new float* [points_num_];
+        for (int i = 0; i < points_num_; ++i)
+            points_[i] = const_cast<float*>(points[i].data());
+#endif
+        // create tree
+        tree_ = new ANNkd_tree(const_cast<float**>(points_), points_num_, 3);
+    }
+
+
     KdTreeSearch_ANN::~KdTreeSearch_ANN() {
 #if COPY_POINT_CLOUD // make a copy of the point cloud when constructing the kd-tree
         annDeallocPts(points_);

easy3d/kdtree/kdtree_search_ann.h

@@ -47,6 +47,12 @@ namespace easy3d {
          */
         explicit KdTreeSearch_ANN(const PointCloud *cloud);
 
+        /**
+         * \brief Constructor.
+         * \param points The points for which a KdTree will be constructed.
+         */
+        explicit KdTreeSearch_ANN(const std::vector<vec3>& points);
+
         ~KdTreeSearch_ANN() override;
 
         /// \name Closest point query

easy3d/kdtree/kdtree_search_eth.cpp

@@ -47,6 +47,17 @@ namespace easy3d {
     }
 
 
+    KdTreeSearch_ETH::KdTreeSearch_ETH(const std::vector<vec3>& points) : KdTreeSearch(points) {
+        // prepare data
+        points_num_ = points.size();
+        points_ = const_cast<float*>(points[0].data());
+
+        // create tree
+        const int maxBucketSize = 16;	// number of points per bucket
+        tree_ = new kdtree::KdTree(reinterpret_cast<kdtree::Vector3D*>(points_), points_num_, maxBucketSize);
+    }
+
+
     KdTreeSearch_ETH::~KdTreeSearch_ETH() {
         delete get_tree(tree_);
         points_ = nullptr;

easy3d/kdtree/kdtree_search_eth.h

@@ -47,6 +47,12 @@ namespace easy3d {
          */
         explicit KdTreeSearch_ETH(const PointCloud *cloud);
 
+        /**
+         * \brief Constructor.
+         * \param points The points for which a KdTree will be constructed.
+         */
+        explicit KdTreeSearch_ETH(const std::vector<vec3>& points);
+
         ~KdTreeSearch_ETH() override;
 
         /// \name Closest point query

easy3d/kdtree/kdtree_search_flann.cpp

@@ -52,6 +52,23 @@ namespace easy3d {
     }
 
 
+    KdTreeSearch_FLANN::KdTreeSearch_FLANN(const std::vector<vec3>& points) : KdTreeSearch(points) {
+        //checks_ = 32;
+        checks_ = flann::FLANN_CHECKS_AUTOTUNED;
+
+        // prepare data
+        points_num_ = int(points.size());
+        points_ = const_cast<float*>(points[0].data());
+
+        // create tree
+        flann::Matrix<float> dataset(points_, points_num_, 3);
+        // construct a single kd-tree optimized for searching lower dimensionality data
+        auto tree = new flann::Index< flann::L2<float> >(dataset, flann::KDTreeSingleIndexParams());
+        tree->buildIndex();
+        tree_ = tree;
+    }
+
+
     KdTreeSearch_FLANN::~KdTreeSearch_FLANN() {
         delete get_tree(tree_);
     }

easy3d/kdtree/kdtree_search_flann.h

@@ -47,6 +47,12 @@ namespace easy3d {
          */
         explicit KdTreeSearch_FLANN(const PointCloud *cloud);
 
+        /**
+         * \brief Constructor.
+         * \param points The points for which a KdTree will be constructed.
+         */
+        explicit KdTreeSearch_FLANN(const std::vector<vec3>& points);
+
         ~KdTreeSearch_FLANN() override;
 
         /**

easy3d/kdtree/kdtree_search_nanoflann.cpp

@@ -81,6 +81,18 @@ namespace easy3d {
     }
 
 
+    KdTreeSearch_NanoFLANN::KdTreeSearch_NanoFLANN(const std::vector<vec3>& points) : KdTreeSearch(points) {
+        // prepare data
+        points_ = const_cast<std::vector<vec3>*>(&points);
+
+        // create tree
+        auto pset = new PointSet(points_);
+        auto tree = new KdTree(pset);
+        tree->buildIndex();
+        tree_ = tree;
+    }
+
+
     KdTreeSearch_NanoFLANN::~KdTreeSearch_NanoFLANN() {
         delete get_tree(tree_);
     }

easy3d/kdtree/kdtree_search_nanoflann.h

@@ -46,6 +46,12 @@ namespace easy3d {
          */
         explicit KdTreeSearch_NanoFLANN(const PointCloud *cloud);
 
+        /**
+         * \brief Constructor.
+         * \param points The points for which a KdTree will be constructed.
+         */
+        explicit KdTreeSearch_NanoFLANN(const std::vector<vec3>& points);
+
         ~KdTreeSearch_NanoFLANN() override;
 
         /// \name Closest point query