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+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's 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.
+//
+//   * The name of the copyright holders may not 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 Intel Corporation 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.
+//
+
+/** @file
+@author Tolga Birdal <tbirdal AT gmail.com>
+*/
+
+#ifndef __OPENCV_SURFACE_MATCHING_HELPERS_HPP__
+#define __OPENCV_SURFACE_MATCHING_HELPERS_HPP__
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace ppf_match_3d
+{
+
+//! @addtogroup surface_matching
+//! @{
+
+/**
+ *  @brief Load a PLY file
+ *  @param [in] fileName The PLY model to read
+ *  @param [in] withNormals Flag wheather the input PLY contains normal information,
+ *  and whether it should be loaded or not
+ *  @return Returns the matrix on successfull load
+ */
+CV_EXPORTS Mat loadPLYSimple(const char* fileName, int withNormals);
+
+/**
+ *  @brief Write a point cloud to PLY file
+ *  @param [in] PC Input point cloud
+ *  @param [in] fileName The PLY model file to write
+*/
+CV_EXPORTS void writePLY(Mat PC, const char* fileName);
+
+/**
+*  @brief Used for debbuging pruposes, writes a point cloud to a PLY file with the tip
+*  of the normal vectors as visible red points
+*  @param [in] PC Input point cloud
+*  @param [in] fileName The PLY model file to write
+*/
+CV_EXPORTS void writePLYVisibleNormals(Mat PC, const char* fileName);
+
+Mat samplePCUniform(Mat PC, int sampleStep);
+Mat samplePCUniformInd(Mat PC, int sampleStep, std::vector<int>& indices);
+
+/**
+ *  Sample a point cloud using uniform steps
+ *  @param [in] pc Input point cloud
+ *  @param [in] xrange X components (min and max) of the bounding box of the model
+ *  @param [in] yrange Y components (min and max) of the bounding box of the model
+ *  @param [in] zrange Z components (min and max) of the bounding box of the model
+ *  @param [in] sample_step_relative The point cloud is sampled such that all points
+ *  have a certain minimum distance. This minimum distance is determined relatively using
+ *  the parameter sample_step_relative. 
+ *  @param [in] weightByCenter The contribution of the quantized data points can be weighted
+ *  by the distance to the origin. This parameter enables/disables the use of weighting.
+ *  @return Sampled point cloud
+*/
+CV_EXPORTS Mat samplePCByQuantization(Mat pc, float xrange[2], float yrange[2], float zrange[2], float sample_step_relative, int weightByCenter=0);
+
+void computeBboxStd(Mat pc, float xRange[2], float yRange[2], float zRange[2]);
+
+void* indexPCFlann(Mat pc);
+void destroyFlann(void* flannIndex);
+void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances);
+void queryPCFlann(void* flannIndex, Mat& pc, Mat& indices, Mat& distances, const int numNeighbors);
+
+/**
+ *  Mostly for visualization purposes. Normalizes the point cloud in a Hartley-Zissermann
+ *  fashion. In other words, the point cloud is centered, and scaled such that the largest
+ *  distance from the origin is sqrt(2). Finally a rescaling is applied.
+ *  @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per
+ *  row are expected.
+ *  @param [in] scale The scale after normalization. Default to 1.
+ *  @return Normalized point cloud
+*/
+CV_EXPORTS Mat normalize_pc(Mat pc, float scale);
+
+Mat normalizePCCoeff(Mat pc, float scale, float* Cx, float* Cy, float* Cz, float* MinVal, float* MaxVal);
+Mat transPCCoeff(Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal);
+
+/**
+ *  Transforms the point cloud with a given a homogeneous 4x4 pose matrix (in double precision)
+ *  @param [in] pc Input point cloud (CV_32F family). Point clouds with 3 or 6 elements per
+ *  row are expected. In the case where the normals are provided, they are also rotated to be
+ *  compatible with the entire transformation
+ *  @param [in] Pose 4x4 pose matrix, but linearized in row-major form.
+ *  @return Transformed point cloud
+*/
+CV_EXPORTS Mat transformPCPose(Mat pc, double Pose[16]);
+
+/**
+ *  Generate a random 4x4 pose matrix
+ *  @param [out] Pose The random pose
+*/
+CV_EXPORTS void getRandomPose(double Pose[16]);
+
+/**
+ *  Adds a uniform noise in the given scale to the input point cloud
+ *  @param [in] pc Input point cloud (CV_32F family). 
+ *  @param [in] scale Input scale of the noise. The larger the scale, the more noisy the output
+*/
+CV_EXPORTS Mat addNoisePC(Mat pc, double scale);
+
+/**
+ *  @brief Compute the normals of an arbitrary point cloud
+ *  computeNormalsPC3d uses a plane fitting approach to smoothly compute
+ *  local normals. Normals are obtained through the eigenvector of the covariance
+ *  matrix, corresponding to the smallest eigen value.
+ *  If PCNormals is provided to be an Nx6 matrix, then no new allocation
+ *  is made, instead the existing memory is overwritten.
+ *  @param [in] PC Input point cloud to compute the normals for.
+ *  @param [in] PCNormals Output point cloud
+ *  @param [in] NumNeighbors Number of neighbors to take into account in a local region
+ *  @param [in] FlipViewpoint Should normals be flipped to a viewing direction?
+ *  @param [in] viewpoint
+ *  @return Returns 0 on success
+ */
+CV_EXPORTS int computeNormalsPC3d(const Mat& PC, Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const double viewpoint[3]);
+
+//! @}
+
+} // namespace ppf_match_3d
+} // namespace cv
+
+#endif