diff --git a/CHANGELOG.txt b/CHANGELOG.txt
index a112e7e8..a7b5d9ac 100644
--- a/CHANGELOG.txt
+++ b/CHANGELOG.txt
@@ -1,4 +1,5 @@
 nanoflann 1.3.0: (Unreleased)
+  * Eigen::Matrix datasets: now uses std::cref() to store a reference to matrix.
   * GSOC2017 contributions by Pranjal Kumar Rai:
     * Support for dynamic datasets.
     * Support for non-Euclidean spaces: SO(2), SO(3)
diff --git a/examples/matrix_example.cpp b/examples/matrix_example.cpp
index 4b2de973..b32d8e03 100644
--- a/examples/matrix_example.cpp
+++ b/examples/matrix_example.cpp
@@ -28,8 +28,8 @@
 
 #include <nanoflann.hpp>
 
-#include <ctime>
 #include <cstdlib>
+#include <ctime>
 #include <iostream>
 
 #include <Eigen/Dense>
@@ -41,77 +41,85 @@ using namespace nanoflann;
 const int SAMPLES_DIM = 15;
 
 template <typename Der>
-void generateRandomPointCloud(Eigen::MatrixBase<Der> &mat, const size_t N, const size_t dim, const typename Der::Scalar max_range = 10)
-{
-	std::cout << "Generating "<< N << " random points...";
-	mat.resize(N,dim);
-	for (size_t i = 0; i < N; i++)
-		for (size_t d = 0; d < dim; d++)
-			mat(i,d) = max_range * (rand() % 1000) / typename Der::Scalar(1000);
-	std::cout << "done\n";
+void generateRandomPointCloud(Eigen::MatrixBase<Der> &mat, const size_t N,
+                              const size_t dim,
+                              const typename Der::Scalar max_range = 10) {
+  std::cout << "Generating " << N << " random points...";
+  mat.resize(N, dim);
+  for (size_t i = 0; i < N; i++)
+    for (size_t d = 0; d < dim; d++)
+      mat(i, d) = max_range * (rand() % 1000) / typename Der::Scalar(1000);
+  std::cout << "done\n";
 }
 
 template <typename num_t>
-void kdtree_demo(const size_t nSamples, const size_t dim)
-{
-	Eigen::Matrix<num_t, Dynamic, Dynamic>  mat(nSamples, dim);
-
-	const num_t max_range = 20;
-
-	// Generate points:
-	generateRandomPointCloud(mat, nSamples, dim, max_range);
-
-//	cout << mat << endl;
-
-	// Query point:
-	std::vector<num_t> query_pt(dim);
-	for (size_t d = 0; d < dim; d++)
-		query_pt[d] = max_range * (rand() % 1000) / num_t(1000);
-
-
-	// ------------------------------------------------------------
-	// construct a kd-tree index:
-	//    Some of the different possibilities (uncomment just one)
-	// ------------------------------------------------------------
-	// Dimensionality set at run-time (default: L2)
-	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t, Dynamic, Dynamic> >  my_kd_tree_t;
-
-	// Dimensionality set at compile-time
-//	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >  my_kd_tree_t;
-
-	// Dimensionality set at compile-time: Explicit selection of the distance metric: L2
-//	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L2>  my_kd_tree_t;
-
-	// Dimensionality set at compile-time: Explicit selection of the distance metric: L2_simple
-//	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L2_Simple>  my_kd_tree_t;
-
-	// Dimensionality set at compile-time: Explicit selection of the distance metric: L1
-//	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L1>  my_kd_tree_t;
-
-	my_kd_tree_t   mat_index(mat, 10 /* max leaf */ );
-	mat_index.index->buildIndex();
-
-	// do a knn search
-	const size_t num_results = 3;
-	vector<size_t>   ret_indexes(num_results);
-	vector<num_t> out_dists_sqr(num_results);
-
-	nanoflann::KNNResultSet<num_t> resultSet(num_results);
-
-	resultSet.init(&ret_indexes[0], &out_dists_sqr[0] );
-	mat_index.index->findNeighbors(resultSet, &query_pt[0], nanoflann::SearchParams(10));
-
-	std::cout << "knnSearch(nn="<<num_results<<"): \n";
-	for (size_t i=0; i<num_results; i++)
-		std::cout << "ret_index["<<i<<"]=" << ret_indexes[i] << " out_dist_sqr=" << out_dists_sqr[i] << endl;
-
+void kdtree_demo(const size_t nSamples, const size_t dim) {
+  Eigen::Matrix<num_t, Dynamic, Dynamic> mat(nSamples, dim);
+
+  const num_t max_range = 20;
+
+  // Generate points:
+  generateRandomPointCloud(mat, nSamples, dim, max_range);
+
+  //	cout << mat << endl;
+
+  // Query point:
+  std::vector<num_t> query_pt(dim);
+  for (size_t d = 0; d < dim; d++)
+    query_pt[d] = max_range * (rand() % 1000) / num_t(1000);
+
+  // ------------------------------------------------------------
+  // construct a kd-tree index:
+  //    Some of the different possibilities (uncomment just one)
+  // ------------------------------------------------------------
+  // Dimensionality set at run-time (default: L2)
+  typedef KDTreeEigenMatrixAdaptor<Eigen::Matrix<num_t, Dynamic, Dynamic>>
+      my_kd_tree_t;
+
+  // Dimensionality set at compile-time
+  //	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >
+  // my_kd_tree_t;
+
+  // Dimensionality set at compile-time: Explicit selection of the distance
+  // metric: L2
+  //	typedef KDTreeEigenMatrixAdaptor<
+  // Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L2>  my_kd_tree_t;
+
+  // Dimensionality set at compile-time: Explicit selection of the distance
+  // metric: L2_simple
+  //	typedef KDTreeEigenMatrixAdaptor<
+  // Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L2_Simple>
+  // my_kd_tree_t;
+
+  // Dimensionality set at compile-time: Explicit selection of the distance
+  // metric: L1
+  //	typedef KDTreeEigenMatrixAdaptor<
+  // Eigen::Matrix<num_t,Dynamic,Dynamic>,nanoflann::metric_L1>  my_kd_tree_t;
+
+  my_kd_tree_t mat_index(dim, std::cref(mat), 10 /* max leaf */);
+  mat_index.index->buildIndex();
+
+  // do a knn search
+  const size_t num_results = 3;
+  vector<size_t> ret_indexes(num_results);
+  vector<num_t> out_dists_sqr(num_results);
+
+  nanoflann::KNNResultSet<num_t> resultSet(num_results);
+
+  resultSet.init(&ret_indexes[0], &out_dists_sqr[0]);
+  mat_index.index->findNeighbors(resultSet, &query_pt[0],
+                                 nanoflann::SearchParams(10));
+
+  std::cout << "knnSearch(nn=" << num_results << "): \n";
+  for (size_t i = 0; i < num_results; i++)
+    std::cout << "ret_index[" << i << "]=" << ret_indexes[i]
+              << " out_dist_sqr=" << out_dists_sqr[i] << endl;
 }
 
-int main(int argc, char** argv)
-{
-	// Randomize Seed
-	srand(time(NULL));
-	kdtree_demo<float>(1e3 /* samples */, SAMPLES_DIM /* dim */);
+int main(int argc, char **argv) {
+  // Randomize Seed
+  srand(time(NULL));
+  kdtree_demo<float>(1e3 /* samples */, SAMPLES_DIM /* dim */);
 
-	return 0;
+  return 0;
 }
diff --git a/include/nanoflann.hpp b/include/nanoflann.hpp
index a9e5f57c..c30b4fc2 100644
--- a/include/nanoflann.hpp
+++ b/include/nanoflann.hpp
@@ -1704,10 +1704,11 @@ class KDTreeSingleIndexDynamicAdaptor_
         if (dist < worst_dist) {
           if (!result_set.addPoint(
                   static_cast<typename RESULTSET::DistanceType>(dist),
-                  static_cast<typename RESULTSET::IndexType>(vind[i]))) {
+                  static_cast<typename RESULTSET::IndexType>(
+                      BaseClassRef::vind[i]))) {
             // the resultset doesn't want to receive any more points, we're done
             // searching!
-            return false;
+            return; // false;
           }
         }
       }
@@ -1941,9 +1942,9 @@ class KDTreeSingleIndexDynamicAdaptor {
  * Distance The distance metric to use: nanoflann::metric_L1,
  * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
  */
-template <class MatrixType, class Distance = nanoflann::metric_L2>
+template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
 struct KDTreeEigenMatrixAdaptor {
-  typedef KDTreeEigenMatrixAdaptor<MatrixType, Distance> self_t;
+  typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance> self_t;
   typedef typename MatrixType::Scalar num_t;
   typedef typename MatrixType::Index IndexType;
   typedef
@@ -1960,7 +1961,7 @@ struct KDTreeEigenMatrixAdaptor {
                            const std::reference_wrapper<const MatrixType> &mat,
                            const int leaf_max_size = 10)
       : m_data_matrix(mat) {
-    const int dims = static_cast<int>(mat.cols());
+    const int dims = static_cast<int>(mat.get().cols());
     if (dims != dimensionality)
       throw std::runtime_error(
           "Error: 'dimensionality' must match column count in data matrix");
@@ -2002,11 +2003,13 @@ struct KDTreeEigenMatrixAdaptor {
   self_t &derived() { return *this; }
 
   // Must return the number of data points
-  inline size_t kdtree_get_point_count() const { return m_data_matrix.rows(); }
+  inline size_t kdtree_get_point_count() const {
+    return m_data_matrix.get().rows();
+  }
 
   // Returns the dim'th component of the idx'th point in the class:
   inline num_t kdtree_get_pt(const IndexType idx, int dim) const {
-    return m_data_matrix.coeff(idx, IndexType(dim));
+    return m_data_matrix.get().coeff(idx, IndexType(dim));
   }
 
   // Optional bounding-box computation: return false to default to a standard