diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTreeTraits.h b/Ponca/src/SpatialPartitioning/KdTree/kdTreeTraits.h
index 88363f4c2..e96675cbb 100644
--- a/Ponca/src/SpatialPartitioning/KdTree/kdTreeTraits.h
+++ b/Ponca/src/SpatialPartitioning/KdTree/kdTreeTraits.h
@@ -83,6 +83,24 @@ struct KdTreeDefaultLeafNode
 
 /*!
  * \brief The node type used by default by the kd-tree.
+ *
+ * It is possible to modify the Inner and Leaf node types by inheritance. For instance, to add a Bounding box to inner
+ * nodes, define a custom inner node type:
+ *
+ * \snippet ponca_customize_kdtree.cpp CustomInnerNodeDefinition
+ *
+ * Define a custom node type to use it, and expose custom data (inner/leaf node are not exposed directly):
+ *
+ * \snippet ponca_customize_kdtree.cpp CustomNodeDefinition
+ *
+ * To use in the KdTree, define a type using the custom node:
+ *
+ * \snippet ponca_customize_kdtree.cpp KdTreeTypeWithCustomNode
+ *
+ * The added attribute can be accessed
+ *
+ * \snippet ponca_customize_kdtree.cpp ReadCustomProperties
+ *
  */
 template <typename Index, typename NodeIndex, typename DataPoint,
           typename LeafSize = Index,
@@ -221,6 +239,8 @@ struct KdTreeDefaultNode : public KdTreeCustomizableNode<Index, NodeIndex, DataP
 /*!
  * \brief The default traits type used by the kd-tree.
  *
+ * \see KdTreeCustomizableNode Helper class to modify Inner/Leaf nodes without redefining a Trait class
+ *
  * \tparam _NodeType Type used to store nodes, set by default to #KdTreeDefaultNode
  */
 template <typename _DataPoint,
diff --git a/doc/src/ponca_module_spatialpartitioning.mdoc b/doc/src/ponca_module_spatialpartitioning.mdoc
index 9abf5fe95..75ed60389 100644
--- a/doc/src/ponca_module_spatialpartitioning.mdoc
+++ b/doc/src/ponca_module_spatialpartitioning.mdoc
@@ -95,7 +95,8 @@ fit.computeWithIds( myDataStructure.range_neighbors(fitInitPos, scale), vectorPo
 
   \subsection spatialpartitioning_kdtree_extending Extending KdTree
   Ponca::KdTreeBase is a customizable version of Ponca::KdTree, which can be controlled using `Traits`.
-  See KdTreeDefaultTraits for customization API.
+  See KdTreeDefaultTraits and KdTreeCustomizableNode for customization APIs. See also:
+   - `examples/cpp/ponca_customize_kdtree.cpp`
 
   \subsection spatialpartitioning_kdtree_implementation Implementation details
 
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
index 48ecf31c2..cac5a580e 100644
--- a/examples/cpp/CMakeLists.txt
+++ b/examples/cpp/CMakeLists.txt
@@ -35,12 +35,20 @@ add_dependencies(ponca-examples ponca_fit_line)
 ponca_handle_eigen_dependency(ponca_fit_line)
 
 set(ponca_neighbor_search_SRCS
-    ponca_neighbor_search.cpp
+        ponca_neighbor_search.cpp
 )
 add_executable(ponca_neighbor_search ${ponca_neighbor_search_SRCS})
 target_include_directories(ponca_neighbor_search PRIVATE ${PONCA_src_ROOT})
 add_dependencies(ponca-examples ponca_neighbor_search)
 ponca_handle_eigen_dependency(ponca_neighbor_search)
 
+set(ponca_customize_kdtree_SRCS
+        ponca_customize_kdtree.cpp
+)
+add_executable(ponca_customize_kdtree ${ponca_customize_kdtree_SRCS})
+target_include_directories(ponca_customize_kdtree PRIVATE ${PONCA_src_ROOT})
+add_dependencies(ponca-examples ponca_customize_kdtree)
+ponca_handle_eigen_dependency(ponca_customize_kdtree)
+
 add_subdirectory(pcl)
 add_subdirectory(nanoflann)
diff --git a/examples/cpp/ponca_customize_kdtree.cpp b/examples/cpp/ponca_customize_kdtree.cpp
new file mode 100644
index 000000000..5619391d7
--- /dev/null
+++ b/examples/cpp/ponca_customize_kdtree.cpp
@@ -0,0 +1,93 @@
+/*
+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 <optional>
+#include <Ponca/SpatialPartitioning>
+#include <Eigen/Core>
+
+struct DataPoint
+{
+    enum {Dim = 3};
+    using Scalar = float;
+    using VectorType = Eigen::Vector<Scalar,Dim>;
+    inline const auto& pos() const {return m_pos;}
+    VectorType m_pos;
+};
+
+//! [CustomInnerNodeDefinition]
+template <typename NodeIndex, typename Scalar, int DIM, typename _AabbType = Eigen::AlignedBox<Scalar, DIM>>
+struct MyKdTreeInnerNode : public Ponca::KdTreeDefaultInnerNode<NodeIndex, Scalar, DIM> {
+    using AabbType = _AabbType;
+    AabbType m_aabb{};
+};
+//! [CustomInnerNodeDefinition]
+
+//! [CustomNodeDefinition]
+template <typename Index, typename NodeIndex, typename DataPoint, typename LeafSize = Index>
+struct MyKdTreeNode : Ponca::KdTreeCustomizableNode<Index, NodeIndex, DataPoint, LeafSize,
+        MyKdTreeInnerNode<NodeIndex, typename DataPoint::Scalar, DataPoint::Dim>> {
+
+    using Base = Ponca::KdTreeCustomizableNode<Index, NodeIndex, DataPoint, LeafSize,
+            MyKdTreeInnerNode<NodeIndex, typename DataPoint::Scalar, DataPoint::Dim>>;
+    using AabbType  = typename Base::AabbType;
+
+    void configure_range(Index start, Index size, const AabbType &aabb)
+    {
+        Base::configure_range(start, size, aabb);
+        if (! Base::is_leaf() )
+        {
+            Base::getAsInner().m_aabb = aabb;
+        }
+    }
+    [[nodiscard]] inline std::optional<AabbType> getAabb() const {
+        if (! Base::is_leaf())
+            return Base::getAsInner().m_aabb;
+        else
+            return std::optional<AabbType>();
+    }
+};
+//! [CustomNodeDefinition]
+
+int main()
+{
+    // generate N random points
+    constexpr int N = 1e5;
+    std::vector<DataPoint> points(N);
+    std::generate(points.begin(), points.end(), [](){
+        return DataPoint{100 * DataPoint::VectorType::Random()};});
+
+//! [KdTreeTypeWithCustomNode]
+    using CustomKdTree = Ponca::KdTreeBase<Ponca::KdTreeDefaultTraits<DataPoint,MyKdTreeNode>>;
+//! [KdTreeTypeWithCustomNode]
+
+    // build the k-d tree
+    const CustomKdTree kdtree(points);
+
+    // 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};
+
+    //
+    // nearest neighbor search
+    //
+    std::cout << "the nearest neighbor of the point at index " << query_idx << " is at index "
+              << *kdtree.nearest_neighbor(query_idx).begin() << std::endl;
+    std::cout << "the nearest neighbor of the point (" << query_pt.transpose() << ") is at index "
+              << *kdtree.nearest_neighbor(query_pt).begin() << std::endl;
+
+    //! [ReadCustomProperties]
+    auto bbox = kdtree.node_data()[0].getAabb();
+    if (bbox) {
+        std::cout << "Root bounding box is as follows: \n"
+                  << "  Center:   " << bbox->center()
+                  << "  Diagonal: " << bbox->diagonal()
+                  << std::endl;
+    }
+    //! [ReadCustomProperties]
+
+    return 1;
+}