diff --git a/include/ceres/numeric_diff_cost_function.h b/include/ceres/numeric_diff_cost_function.h
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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)
+//
+// Create CostFunctions as needed by the least squares framework with jacobians
+// computed via numeric (a.k.a. finite) differentiation. For more details see
+// http://en.wikipedia.org/wiki/Numerical_differentiation.
+//
+// To get a numerically differentiated cost function, define a subclass of
+// CostFunction such that the Evaluate() function ignores the jacobian
+// parameter. The numeric differentiation wrapper will fill in the jacobian
+// parameter if nececssary by repeatedly calling the Evaluate() function with
+// small changes to the appropriate parameters, and computing the slope. For
+// performance, the numeric differentiation wrapper class is templated on the
+// concrete cost function, even though it could be implemented only in terms of
+// the virtual CostFunction interface.
+//
+// The numerically differentiated version of a cost function for a cost function
+// can be constructed as follows:
+//
+//   CostFunction* cost_function
+//       = new NumericDiffCostFunction<MyCostFunction, CENTRAL, 1, 4, 8>(
+//           new MyCostFunction(...), TAKE_OWNERSHIP);
+//
+// where MyCostFunction has 1 residual and 2 parameter blocks with sizes 4 and 8
+// respectively. Look at the tests for a more detailed example.
+//
+// The central difference method is considerably more accurate at the cost of
+// twice as many function evaluations than forward difference. Consider using
+// central differences begin with, and only after that works, trying forward
+// difference to improve performance.
+//
+// TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives.
+
+#ifndef CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
+#define CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_
+
+#include <cstring>
+#include <glog/logging.h>
+#include "Eigen/Dense"
+#include "ceres/internal/scoped_ptr.h"
+#include "ceres/sized_cost_function.h"
+#include "ceres/types.h"
+
+namespace ceres {
+
+enum NumericDiffMethod {
+  CENTRAL,
+  FORWARD,
+};
+
+// This is split from the main class because C++ doesn't allow partial template
+// specializations for member functions. The alternative is to repeat the main
+// class for differing numbers of parameters, which is also unfortunate.
+template <typename CostFunctionNoJacobian,
+          int num_residuals,
+          int parameter_block_size,
+          int parameter_block,
+          NumericDiffMethod method>
+struct Differencer {
+  // Mutates parameters but must restore them before return.
+  static bool EvaluateJacobianForParameterBlock(
+      const CostFunctionNoJacobian *function,
+      double const* residuals_at_eval_point,
+      double **parameters,
+      double **jacobians) {
+    using Eigen::Map;
+    using Eigen::Matrix;
+    using Eigen::RowMajor;
+
+    typedef Matrix<double, num_residuals, 1> ResidualVector;
+    typedef Matrix<double, parameter_block_size, 1> ParameterVector;
+    typedef Matrix<double, num_residuals, parameter_block_size, RowMajor>
+        JacobianMatrix;
+
+    Map<JacobianMatrix> parameter_jacobian(jacobians[parameter_block],
+                                           num_residuals,
+                                           parameter_block_size);
+
+    // Mutate 1 element at a time and then restore.
+    Map<ParameterVector> x_plus_delta(parameters[parameter_block],
+                                      parameter_block_size);
+    ParameterVector x(x_plus_delta);
+
+    // TODO(keir): Pick a smarter number! In theory a good choice is sqrt(eps) *
+    // x, which for doubles means about 1e-8 * x. However, I have found this
+    // number too optimistic. This number should be exposed for users to change.
+    const double kRelativeStepSize = 1e-6;
+
+    ParameterVector step_size = x.array().abs() * kRelativeStepSize;
+
+    // To handle cases where a parameter 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. Take
+      // kRelativeStepSize as a guess and hope for the best.
+      fallback_step_size = kRelativeStepSize;
+    }
+
+    // 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);
+
+      double residuals[num_residuals];  // NOLINT
+      if (!function->Evaluate(parameters, residuals, 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) =
+          Map<const ResidualVector>(residuals, num_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, NULL)) {
+          // Something went wrong; bail.
+          return false;
+        }
+        parameter_jacobian.col(j) -=
+            Map<ResidualVector>(residuals, num_residuals, 1);
+        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;
+  }
+};
+
+// Prevent invalid instantiations.
+template <typename CostFunctionNoJacobian,
+          int num_residuals,
+          int parameter_block,
+          NumericDiffMethod method>
+struct Differencer<CostFunctionNoJacobian,
+                  num_residuals,
+                  0 /* parameter_block_size */,
+                  parameter_block,
+                  method> {
+  static bool EvaluateJacobianForParameterBlock(
+      const CostFunctionNoJacobian *function,
+      double const* residuals_at_eval_point,
+      double **parameters,
+      double **jacobians) {
+    LOG(FATAL) << "Shouldn't get here.";
+    return true;
+  }
+};
+
+template <typename CostFunctionNoJacobian,
+         NumericDiffMethod method = CENTRAL, int M = 0,
+         int N0 = 0, int N1 = 0, int N2 = 0, int N3 = 0, int N4 = 0, int N5 = 0>
+class NumericDiffCostFunction
+    : public SizedCostFunction<M, N0, N1, N2, N3, N4, N5> {
+ public:
+  NumericDiffCostFunction(CostFunctionNoJacobian* function,
+                          Ownership ownership)
+      : function_(function), ownership_(ownership) {}
+
+  virtual ~NumericDiffCostFunction() {
+    if (ownership_ != TAKE_OWNERSHIP) {
+      function_.release();
+    }
+  }
+
+  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;
+    }
+
+    // Create a copy of the parameters which will get mutated.
+    const int kParametersSize = N0 + N1 + N2 + N3 + N4 + N5;
+    double parameters_copy[kParametersSize];
+    double *parameters_references_copy[6];
+    parameters_references_copy[0] = &parameters_copy[0];
+    parameters_references_copy[1] = &parameters_copy[0] + N0;
+    parameters_references_copy[2] = &parameters_copy[0] + N0 + N1;
+    parameters_references_copy[3] = &parameters_copy[0] + N0 + N1 + N2;
+    parameters_references_copy[4] = &parameters_copy[0] + N0 + N1 + N2 + N3;
+    parameters_references_copy[5] =
+        &parameters_copy[0] + N0 + N1 + N2 + N3 + N4;
+
+#define COPY_PARAMETER_BLOCK(block) \
+    if (N ## block) memcpy(parameters_references_copy[block], \
+                           parameters[block], \
+                           sizeof(double) * N ## block);  // NOLINT
+    COPY_PARAMETER_BLOCK(0);
+    COPY_PARAMETER_BLOCK(1);
+    COPY_PARAMETER_BLOCK(2);
+    COPY_PARAMETER_BLOCK(3);
+    COPY_PARAMETER_BLOCK(4);
+    COPY_PARAMETER_BLOCK(5);
+#undef COPY_PARAMETER_BLOCK
+
+#define EVALUATE_JACOBIAN_FOR_BLOCK(block) \
+    if (N ## block && jacobians[block]) { \
+      if (!Differencer<CostFunctionNoJacobian, /* NOLINT */ \
+                       M, \
+                       N ## block, \
+                       block, \
+                       method>::EvaluateJacobianForParameterBlock( \
+          function_.get(), \
+          residuals, \
+          parameters_references_copy, \
+          jacobians)) { \
+        return false; \
+      } \
+    }
+    EVALUATE_JACOBIAN_FOR_BLOCK(0);
+    EVALUATE_JACOBIAN_FOR_BLOCK(1);
+    EVALUATE_JACOBIAN_FOR_BLOCK(2);
+    EVALUATE_JACOBIAN_FOR_BLOCK(3);
+    EVALUATE_JACOBIAN_FOR_BLOCK(4);
+    EVALUATE_JACOBIAN_FOR_BLOCK(5);
+#undef EVALUATE_JACOBIAN_FOR_BLOCK
+    return true;
+  }
+
+ private:
+  internal::scoped_ptr<CostFunctionNoJacobian> function_;
+  Ownership ownership_;
+};
+
+}  // namespace ceres
+
+#endif  // CERES_PUBLIC_NUMERIC_DIFF_COST_FUNCTION_H_