diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.h b/Ponca/src/SpatialPartitioning/KdTree/kdTree.h
index df9aaf24..9a063db1 100644
--- a/Ponca/src/SpatialPartitioning/KdTree/kdTree.h
+++ b/Ponca/src/SpatialPartitioning/KdTree/kdTree.h
@@ -282,7 +282,8 @@ public :
     
     // Utilities ---------------------------------------------------------------
 public:
-    inline bool valid() const;
+    /// \param ignore_duplicates By default, valid KdTree can have duplicated samples.
+    inline bool valid(bool ignore_duplicates = true) const;
     inline void print(std::ostream& os, bool verbose = false) const;
 
     // Data --------------------------------------------------------------------
diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp b/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp
index 255d6f07..b2fd5aed 100644
--- a/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp
+++ b/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp
@@ -25,11 +25,13 @@ void KdTreeBase<Traits>::clear()
 }
 
 template<typename Traits>
-bool KdTreeBase<Traits>::valid() const
+bool KdTreeBase<Traits>::valid(bool ignore_duplicates) const
 {
+    // Check if point container is empty
     if (m_points.empty())
         return m_nodes.empty() && m_indices.empty();
 
+    // Check that at least one node has been created, and index container is not empty
     if(m_nodes.empty() || m_indices.empty())
     {
         return false;
@@ -38,7 +40,7 @@ bool KdTreeBase<Traits>::valid() const
     std::vector<bool> b(point_count(), false);
     for(IndexType idx : m_indices)
     {
-        if(idx < 0 || point_count() <= idx || b[idx])
+        if(idx < 0 || point_count() <= idx || ( ignore_duplicates ? false : b[idx]))
         {
             return false;
         }
@@ -154,6 +156,8 @@ void KdTreeBase<Traits>::build_rec(NodeIndexType node_id, IndexType start, Index
     node.set_is_leaf(
         end-start <= m_min_cell_size ||
         level >= Traits::MAX_DEPTH ||
+        // Stop descending if the content of the node is not one unique point
+        aabb.diagonal().squaredNorm() <= Eigen::NumTraits<Scalar>::epsilon() ||
         // Since we add 2 nodes per inner node we need to stop if we can't add
         // them both
         (NodeIndexType)m_nodes.size() > MAX_NODE_COUNT - 2);
diff --git a/tests/src/kdtree.cpp b/tests/src/kdtree.cpp
index aac0b096..82022721 100644
--- a/tests/src/kdtree.cpp
+++ b/tests/src/kdtree.cpp
@@ -71,6 +71,35 @@ void testKdtreeWithDuplicate()
         test_tree(points, ids, cellSize);
     }
 
+    // Generate a small part of duplicates by extending the index container
+    {
+        const int nbDuplicates = N;
+
+        ids.resize(nbDuplicates+N);
+        std::generate(ids.begin()+N, ids.end(), [N]() {return Eigen::internal::random<int>(0,N-1); });
+
+        test_tree(points, ids, cellSize);
+    }
+
+    // Generate duplicated coordinates samples TODO
+    //    {
+//        const int nbDuplicates = N/10;
+//        const int nbUniques = N;
+//
+//        auto points = VectorContainer(nbUniques);
+//        std::generate(points.begin(), points.end(), []() {return DataPoint(VectorType::Random()); });
+//
+//        typename KdTreeDense<DataPoint>::IndexContainer ids(nbUniques);
+//        std::iota(ids.begin(), ids.end(), 0);
+//        ids.resize(nbDuplicates*nbUniques);
+//
+//        for (int i = 1; i < nbDuplicates; ++i)
+//        {
+//            std::copy(ids.begin(), ids.begin() + nbUniques, ids.begin()+(nbUniques*i));
+//        }
+//        test_tree(points, ids, cellSize);
+//    }
+
 }
 
 template<typename NodeType>