diff --git a/internal/ceres/runtime_numeric_diff_cost_function.cc b/internal/ceres/runtime_numeric_diff_cost_function.cc
new file mode 100644
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+++ b/internal/ceres/runtime_numeric_diff_cost_function.cc
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
+// http://code.google.com/p/ceres-solver/
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are met:
+//
+// * Redistributions of source code must retain the above copyright notice,
+//   this list of conditions and the following disclaimer.
+// * Redistributions in binary form must reproduce the above copyright notice,
+//   this list of conditions and the following disclaimer in the documentation
+//   and/or other materials provided with the distribution.
+// * Neither the name of Google Inc. nor the names of its contributors may be
+//   used to endorse or promote products derived from this software without
+//   specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+//
+// Author: keir@google.com (Keir Mierle)
+//
+// Based on the templated version in public/numeric_diff_cost_function.h.
+
+#include "ceres/runtime_numeric_diff_cost_function.h"
+
+#include <algorithm>
+#include <numeric>
+#include <vector>
+
+#include <glog/logging.h>
+#include "Eigen/Dense"
+#include "ceres/cost_function.h"
+#include "ceres/internal/scoped_ptr.h"
+
+namespace ceres {
+namespace internal {
+namespace {
+
+bool EvaluateJacobianForParameterBlock(const CostFunction* function,
+                                       int parameter_block_size,
+                                       int parameter_block,
+                                       RuntimeNumericDiffMethod method,
+                                       double relative_step_size,
+                                       double const* residuals_at_eval_point,
+                                       double** parameters,
+                                       double** jacobians) {
+  using Eigen::Map;
+  using Eigen::Matrix;
+  using Eigen::Dynamic;
+  using Eigen::RowMajor;
+
+  typedef Matrix<double, Dynamic, 1> ResidualVector;
+  typedef Matrix<double, Dynamic, 1> ParameterVector;
+  typedef Matrix<double, Dynamic, Dynamic, RowMajor> JacobianMatrix;
+
+  int num_residuals = function->num_residuals();
+
+  Map<JacobianMatrix> parameter_jacobian(jacobians[parameter_block],
+                                         num_residuals,
+                                         parameter_block_size);
+
+  // Mutate one element at a time and then restore.
+  Map<ParameterVector> x_plus_delta(parameters[parameter_block],
+                                    parameter_block_size);
+  ParameterVector x(x_plus_delta);
+  ParameterVector step_size = x.array().abs() * relative_step_size;
+
+  // To handle cases where a paremeter is exactly zero, instead use the mean
+  // step_size for the other dimensions.
+  double fallback_step_size = step_size.sum() / step_size.rows();
+  if (fallback_step_size == 0.0) {
+    // If all the parameters are zero, there's no good answer. Use the given
+    // relative step_size as absolute step_size and hope for the best.
+    fallback_step_size = relative_step_size;
+  }
+
+  // For each parameter in the parameter block, use finite differences to
+  // compute the derivative for that parameter.
+  for (int j = 0; j < parameter_block_size; ++j) {
+    if (step_size(j) == 0.0) {
+      // The parameter is exactly zero, so compromise and use the mean step_size
+      // from the other parameters. This can break in many cases, but it's hard
+      // to pick a good number without problem specific knowledge.
+      step_size(j) = fallback_step_size;
+    }
+    x_plus_delta(j) = x(j) + step_size(j);
+
+    ResidualVector residuals(num_residuals);
+    if (!function->Evaluate(parameters, &residuals[0], NULL)) {
+      // Something went wrong; bail.
+      return false;
+    }
+
+    // Compute this column of the jacobian in 3 steps:
+    // 1. Store residuals for the forward part.
+    // 2. Subtract residuals for the backward (or 0) part.
+    // 3. Divide out the run.
+    parameter_jacobian.col(j) = residuals;
+
+    double one_over_h = 1 / step_size(j);
+    if (method == CENTRAL) {
+      // Compute the function on the other side of x(j).
+      x_plus_delta(j) = x(j) - step_size(j);
+
+      if (!function->Evaluate(parameters, &residuals[0], NULL)) {
+        // Something went wrong; bail.
+        return false;
+      }
+      parameter_jacobian.col(j) -= residuals;
+      one_over_h /= 2;
+    } else {
+      // Forward difference only; reuse existing residuals evaluation.
+      parameter_jacobian.col(j) -=
+          Map<const ResidualVector>(residuals_at_eval_point, num_residuals);
+    }
+    x_plus_delta(j) = x(j);  // Restore x_plus_delta.
+
+    // Divide out the run to get slope.
+    parameter_jacobian.col(j) *= one_over_h;
+  }
+  return true;
+}
+
+class RuntimeNumericDiffCostFunction : public CostFunction {
+ public:
+  RuntimeNumericDiffCostFunction(const CostFunction* function,
+                                 RuntimeNumericDiffMethod method,
+                                 double relative_step_size)
+      : function_(function),
+        method_(method),
+        relative_step_size_(relative_step_size) {
+    *mutable_parameter_block_sizes() = function->parameter_block_sizes();
+    set_num_residuals(function->num_residuals());
+  }
+
+  virtual ~RuntimeNumericDiffCostFunction() { }
+
+  virtual bool Evaluate(double const* const* parameters,
+                        double* residuals,
+                        double** jacobians) const {
+    // Get the function value (residuals) at the the point to evaluate.
+    bool success = function_->Evaluate(parameters, residuals, NULL);
+    if (!success) {
+      // Something went wrong; ignore the jacobian.
+      return false;
+    }
+    if (!jacobians) {
+      // Nothing to do; just forward.
+      return true;
+    }
+
+    const vector<int16>& block_sizes = function_->parameter_block_sizes();
+    CHECK(!block_sizes.empty());
+
+    // Create local space for a copy of the parameters which will get mutated.
+    int parameters_size = accumulate(block_sizes.begin(), block_sizes.end(), 0);
+    vector<double> parameters_copy(parameters_size);
+    vector<double*> parameters_references_copy(block_sizes.size());
+    parameters_references_copy[0] = &parameters_copy[0];
+    for (int block = 1; block < block_sizes.size(); ++block) {
+      parameters_references_copy[block] = parameters_references_copy[block - 1]
+          + block_sizes[block - 1];
+    }
+
+    // Copy the parameters into the local temp space.
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      memcpy(parameters_references_copy[block],
+             parameters[block],
+             block_sizes[block] * sizeof(*parameters[block]));
+    }
+
+    for (int block = 0; block < block_sizes.size(); ++block) {
+      if (!jacobians[block]) {
+        // No jacobian requested for this parameter / residual pair.
+        continue;
+      }
+      if (!EvaluateJacobianForParameterBlock(function_,
+                                             block_sizes[block],
+                                             block,
+                                             method_,
+                                             relative_step_size_,
+                                             residuals,
+                                             &parameters_references_copy[0],
+                                             jacobians)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+ private:
+  const CostFunction* function_;
+  RuntimeNumericDiffMethod method_;
+  double relative_step_size_;
+};
+
+}  // namespace
+
+CostFunction* CreateRuntimeNumericDiffCostFunction(
+    const CostFunction* cost_function,
+    RuntimeNumericDiffMethod method,
+    double relative_step_size) {
+  return new RuntimeNumericDiffCostFunction(cost_function,
+                                            method,
+                                            relative_step_size);
+}
+
+}  // namespace internal
+}  // namespace ceres