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#ifndef PCL_MLS_H_
#define PCL_MLS_H_
// PCL includes
#include <pcl/pcl_base.h>
#include <pcl/search/pcl_search.h>
#include <pcl/common/common.h>
#include <pcl/surface/boost.h>
#include <pcl/surface/eigen.h>
#include <pcl/surface/processing.h>
#include <map>
#include <boost/function.hpp>
namespace pcl
{
/** \brief Data structure used to store the results of the MLS fitting */
struct MLSResult
{
enum ProjectionMethod
{
NONE, /**< \brief Project to the mls plane. */
SIMPLE, /**< \brief Project along the mls plane normal to the polynomial surface. */
ORTHOGONAL /**< \brief Project to the closest point on the polynonomial surface. */
};
/** \brief Data structure used to store the MLS polynomial partial derivatives */
struct PolynomialPartialDerivative
{
double z; /**< \brief The z component of the polynomial evaluated at z(u, v). */
double z_u; /**< \brief The partial derivative dz/du. */
double z_v; /**< \brief The partial derivative dz/dv. */
double z_uu; /**< \brief The partial derivative d^2z/du^2. */
double z_vv; /**< \brief The partial derivative d^2z/dv^2. */
double z_uv; /**< \brief The partial derivative d^2z/dudv. */
};
/** \brief Data structure used to store the MLS projection results */
struct MLSProjectionResults
{
MLSProjectionResults () : u (0), v (0) {}
double u; /**< \brief The u-coordinate of the projected point in local MLS frame. */
double v; /**< \brief The u-coordinate of the projected point in local MLS frame. */
Eigen::Vector3d point; /**< \brief The projected point. */
Eigen::Vector3d normal; /**< \brief The projected point's normal. */
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
inline
MLSResult () : num_neighbors (0), curvature (0.0f), order (0), valid (false) {}
inline
MLSResult (const Eigen::Vector3d &a_query_point,
const Eigen::Vector3d &a_mean,
const Eigen::Vector3d &a_plane_normal,
const Eigen::Vector3d &a_u,
const Eigen::Vector3d &a_v,
const Eigen::VectorXd &a_c_vec,
const int a_num_neighbors,
const float a_curvature,
const int a_order);
/** \brief Given a point calculate it's 3D location in the MLS frame.
* \param[in] pt The point
* \param[out] u The u-coordinate of the point in local MLS frame.
* \param[out] v The v-coordinate of the point in local MLS frame.
* \param[out] w The w-coordinate of the point in local MLS frame.
*/
inline void
getMLSCoordinates (const Eigen::Vector3d &pt, double &u, double &v, double &w) const;
/** \brief Given a point calculate it's 2D location in the MLS frame.
* \param[in] pt The point
* \param[out] u The u-coordinate of the point in local MLS frame.
* \param[out] v The v-coordinate of the point in local MLS frame.
*/
inline void
getMLSCoordinates (const Eigen::Vector3d &pt, double &u, double &v) const;
/** \brief Calculate the polynomial
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \return The polynomial value at the provide uv coordinates.
*/
inline double
getPolynomialValue (const double u, const double v) const;
/** \brief Calculate the polynomial's first and second partial derivatives.
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \return The polynomial partial derivatives at the provide uv coordinates.
*/
inline PolynomialPartialDerivative
getPolynomialPartialDerivative (const double u, const double v) const;
/** \brief Calculate the principle curvatures using the polynomial surface.
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \return The principle curvature [k1, k2] at the provided ub coordinates.
* \note If an error occurs the MLS_MINIMUM_PRINCIPLE_CURVATURE is returned.
*/
inline Eigen::Vector2f
calculatePrincipleCurvatures (const double u, const double v) const;
/** \brief Project a point orthogonal to the polynomial surface.
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \param[in] w The w-coordinate of the point in local MLS frame.
* \return The MLSProjectionResults for the input data.
* \note If the MLSResults does not contain polynomial data it projects the point onto the mls plane.
* \note If the optimization diverges it performs a simple projection on to the polynomial surface.
* \note This was implemented based on this https://math.stackexchange.com/questions/1497093/shortest-distance-between-point-and-surface
*/
inline MLSProjectionResults
projectPointOrthogonalToPolynomialSurface (const double u, const double v, const double w) const;
/** \brief Project a point onto the MLS plane.
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \return The MLSProjectionResults for the input data.
*/
inline MLSProjectionResults
projectPointToMLSPlane (const double u, const double v) const;
/** \brief Project a point along the MLS plane normal to the polynomial surface.
* \param[in] u The u-coordinate of the point in local MLS frame.
* \param[in] v The v-coordinate of the point in local MLS frame.
* \return The MLSProjectionResults for the input data.
* \note If the MLSResults does not contain polynomial data it projects the point onto the mls plane.
*/
inline MLSProjectionResults
projectPointSimpleToPolynomialSurface (const double u, const double v) const;
/**
* \brief Project a point using the specified method.
* \param[in] pt The point to be project.
* \param[in] method The projection method to be used.
* \param[in] required_neighbors The minimum number of neighbors required.
* \note If required_neighbors then any number of neighbors is allowed.
* \note If required_neighbors is not satisfied it projects to the mls plane.
* \return The MLSProjectionResults for the input data.
*/
inline MLSProjectionResults
projectPoint (const Eigen::Vector3d &pt, ProjectionMethod method, int required_neighbors = 0) const;
/**
* \brief Project the query point used to generate the mls surface about using the specified method.
* \param[in] method The projection method to be used.
* \param[in] required_neighbors The minimum number of neighbors required.
* \note If required_neighbors then any number of neighbors is allowed.
* \note If required_neighbors is not satisfied it projects to the mls plane.
* \return The MLSProjectionResults for the input data.
*/
inline MLSProjectionResults
projectQueryPoint (ProjectionMethod method, int required_neighbors = 0) const;
/** \brief Smooth a given point and its neighborghood using Moving Least Squares.
* \param[in] index the index of the query point in the input cloud
* \param[in] nn_indices the set of nearest neighbors indices for pt
* \param[in] search_radius the search radius used to find nearest neighbors for pt
* \param[in] polynomial_order the order of the polynomial to fit to the nearest neighbors
* \param[in] weight_func defines the weight function for the polynomial fit
*/
template <typename PointT> void
computeMLSSurface (const pcl::PointCloud<PointT> &cloud,
int index,
const std::vector<int> &nn_indices,
double search_radius,
int polynomial_order = 2,
boost::function<double(const double)> weight_func = 0);
Eigen::Vector3d query_point; /**< \brief The query point about which the mls surface was generated */
Eigen::Vector3d mean; /**< \brief The mean point of all the neighbors. */
Eigen::Vector3d plane_normal; /**< \brief The normal of the local plane of the query point. */
Eigen::Vector3d u_axis; /**< \brief The axis corresponding to the u-coordinates of the local plane of the query point. */
Eigen::Vector3d v_axis; /**< \brief The axis corresponding to the v-coordinates of the local plane of the query point. */
Eigen::VectorXd c_vec; /**< \brief The polynomial coefficients Example: z = c_vec[0] + c_vec[1]*v + c_vec[2]*v^2 + c_vec[3]*u + c_vec[4]*u*v + c_vec[5]*u^2 */
int num_neighbors; /**< \brief The number of neighbors used to create the mls surface. */
float curvature; /**< \brief The curvature at the query point. */
int order; /**< \brief The order of the polynomial. If order > 1 then use polynomial fit */
bool valid; /**< \brief If True, the mls results data is valid, otherwise False. */
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
/**
* \brief The default weight function used when fitting a polynomial surface
* \param sq_dist the squared distance from a point to origin of the mls frame
* \param sq_mls_radius the squraed mls search radius used
* \return The weight for a point at squared distance from the origin of the mls frame
*/
inline
double computeMLSWeight (const double sq_dist, const double sq_mls_radius) { return (exp (-sq_dist / sq_mls_radius)); }
};
/** \brief MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm
* for data smoothing and improved normal estimation. It also contains methods for upsampling the
* resulting cloud based on the parametric fit.
* Reference paper: "Computing and Rendering Point Set Surfaces" by Marc Alexa, Johannes Behr,
* Daniel Cohen-Or, Shachar Fleishman, David Levin and Claudio T. Silva
* www.sci.utah.edu/~shachar/Publications/crpss.pdf
* \note There is a parallelized version of the processing step, using the OpenMP standard.
* Compared to the standard version, an overhead is incurred in terms of runtime and memory usage.
* The upsampling methods DISTINCT_CLOUD and VOXEL_GRID_DILATION are not parallelized completely,
* i.e. parts of the algorithm run on a single thread only.
* \author Zoltan Csaba Marton, Radu B. Rusu, Alexandru E. Ichim, Suat Gedikli, Robert Huitl
* \ingroup surface
*/
template <typename PointInT, typename PointOutT>
class MovingLeastSquares : public CloudSurfaceProcessing<PointInT, PointOutT>
{
public:
typedef boost::shared_ptr<MovingLeastSquares<PointInT, PointOutT> > Ptr;
typedef boost::shared_ptr<const MovingLeastSquares<PointInT, PointOutT> > ConstPtr;
using PCLBase<PointInT>::input_;
using PCLBase<PointInT>::indices_;
using PCLBase<PointInT>::fake_indices_;
using PCLBase<PointInT>::initCompute;
using PCLBase<PointInT>::deinitCompute;
typedef typename pcl::search::Search<PointInT> KdTree;
typedef typename pcl::search::Search<PointInT>::Ptr KdTreePtr;
typedef pcl::PointCloud<pcl::Normal> NormalCloud;
typedef pcl::PointCloud<pcl::Normal>::Ptr NormalCloudPtr;
typedef pcl::PointCloud<PointOutT> PointCloudOut;
typedef typename PointCloudOut::Ptr PointCloudOutPtr;
typedef typename PointCloudOut::ConstPtr PointCloudOutConstPtr;
typedef pcl::PointCloud<PointInT> PointCloudIn;
typedef typename PointCloudIn::Ptr PointCloudInPtr;
typedef typename PointCloudIn::ConstPtr PointCloudInConstPtr;
typedef boost::function<int (int, double, std::vector<int> &, std::vector<float> &)> SearchMethod;
enum UpsamplingMethod
{
NONE, /**< \brief No upsampling will be done, only the input points will be projected
to their own MLS surfaces. */
DISTINCT_CLOUD, /**< \brief Project the points of the distinct cloud to the MLS surface. */
SAMPLE_LOCAL_PLANE, /**< \brief The local plane of each input point will be sampled in a circular fashion
using the \ref upsampling_radius_ and the \ref upsampling_step_ parameters. */
RANDOM_UNIFORM_DENSITY, /**< \brief The local plane of each input point will be sampled using an uniform random
distribution such that the density of points is constant throughout the
cloud - given by the \ref desired_num_points_in_radius_ parameter. */
VOXEL_GRID_DILATION /**< \brief The input cloud will be inserted into a voxel grid with voxels of
size \ref voxel_size_; this voxel grid will be dilated \ref dilation_iteration_num_
times and the resulting points will be projected to the MLS surface
of the closest point in the input cloud; the result is a point cloud
with filled holes and a constant point density. */
};
/** \brief Empty constructor. */
MovingLeastSquares () : CloudSurfaceProcessing<PointInT, PointOutT> (),
normals_ (),
distinct_cloud_ (),
search_method_ (),
tree_ (),
order_ (2),
search_radius_ (0.0),
sqr_gauss_param_ (0.0),
compute_normals_ (false),
upsample_method_ (NONE),
upsampling_radius_ (0.0),
upsampling_step_ (0.0),
desired_num_points_in_radius_ (0),
cache_mls_results_ (true),
mls_results_ (),
projection_method_ (MLSResult::SIMPLE),
threads_ (1),
voxel_size_ (1.0),
dilation_iteration_num_ (0),
nr_coeff_ (),
corresponding_input_indices_ (),
rng_alg_ (),
rng_uniform_distribution_ ()
{};
/** \brief Empty destructor */
virtual ~MovingLeastSquares () {}
/** \brief Set whether the algorithm should also store the normals computed
* \note This is optional, but need a proper output cloud type
*/
inline void
setComputeNormals (bool compute_normals) { compute_normals_ = compute_normals; }
/** \brief Provide a pointer to the search object.
* \param[in] tree a pointer to the spatial search object.
*/
inline void
setSearchMethod (const KdTreePtr &tree)
{
tree_ = tree;
// Declare the search locator definition
int (KdTree::*radiusSearch)(int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn) const = &KdTree::radiusSearch;
search_method_ = boost::bind (radiusSearch, boost::ref (tree_), _1, _2, _3, _4, 0);
}
/** \brief Get a pointer to the search method used. */
inline KdTreePtr
getSearchMethod () const { return (tree_); }
/** \brief Set the order of the polynomial to be fit.
* \param[in] order the order of the polynomial
* \note Setting order > 1 indicates using a polynomial fit.
*/
inline void
setPolynomialOrder (int order) { order_ = order; }
/** \brief Get the order of the polynomial to be fit. */
inline int
getPolynomialOrder () const { return (order_); }
/** \brief Sets whether the surface and normal are approximated using a polynomial, or only via tangent estimation.
* \param[in] polynomial_fit set to true for polynomial fit
*/
PCL_DEPRECATED ("[pcl::surface::MovingLeastSquares::setPolynomialFit] setPolynomialFit is deprecated. Please use setPolynomialOrder instead.")
inline void
setPolynomialFit (bool polynomial_fit)
{
if (polynomial_fit)
{
if (order_ < 2)
{
order_ = 2;
}
}
else
{
order_ = 0;
}
}
/** \brief Get the polynomial_fit value (true if the surface and normal are approximated using a polynomial). */
PCL_DEPRECATED ("[pcl::surface::MovingLeastSquares::getPolynomialFit] getPolynomialFit is deprecated. Please use getPolynomialOrder instead.")
inline bool
getPolynomialFit () const { return (order_ > 1); }
/** \brief Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting.
* \param[in] radius the sphere radius that is to contain all k-nearest neighbors
* \note Calling this method resets the squared Gaussian parameter to radius * radius !
*/
inline void
setSearchRadius (double radius) { search_radius_ = radius; sqr_gauss_param_ = search_radius_ * search_radius_; }
/** \brief Get the sphere radius used for determining the k-nearest neighbors. */
inline double
getSearchRadius () const { return (search_radius_); }
/** \brief Set the parameter used for distance based weighting of neighbors (the square of the search radius works
* best in general).
* \param[in] sqr_gauss_param the squared Gaussian parameter
*/
inline void
setSqrGaussParam (double sqr_gauss_param) { sqr_gauss_param_ = sqr_gauss_param; }
/** \brief Get the parameter for distance based weighting of neighbors. */
inline double
getSqrGaussParam () const { return (sqr_gauss_param_); }
/** \brief Set the upsampling method to be used
* \param method
*/
inline void
setUpsamplingMethod (UpsamplingMethod method) { upsample_method_ = method; }
/** \brief Set the distinct cloud used for the DISTINCT_CLOUD upsampling method. */
inline void
setDistinctCloud (PointCloudInConstPtr distinct_cloud) { distinct_cloud_ = distinct_cloud; }
/** \brief Get the distinct cloud used for the DISTINCT_CLOUD upsampling method. */
inline PointCloudInConstPtr
getDistinctCloud () const { return (distinct_cloud_); }
/** \brief Set the radius of the circle in the local point plane that will be sampled
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
* \param[in] radius the radius of the circle
*/
inline void
setUpsamplingRadius (double radius) { upsampling_radius_ = radius; }
/** \brief Get the radius of the circle in the local point plane that will be sampled
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
*/
inline double
getUpsamplingRadius () const { return (upsampling_radius_); }
/** \brief Set the step size for the local plane sampling
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
* \param[in] step_size the step size
*/
inline void
setUpsamplingStepSize (double step_size) { upsampling_step_ = step_size; }
/** \brief Get the step size for the local plane sampling
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
*/
inline double
getUpsamplingStepSize () const { return (upsampling_step_); }
/** \brief Set the parameter that specifies the desired number of points within the search radius
* \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
* \param[in] desired_num_points_in_radius the desired number of points in the output cloud in a sphere of
* radius \ref search_radius_ around each point
*/
inline void
setPointDensity (int desired_num_points_in_radius) { desired_num_points_in_radius_ = desired_num_points_in_radius; }
/** \brief Get the parameter that specifies the desired number of points within the search radius
* \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
*/
inline int
getPointDensity () const { return (desired_num_points_in_radius_); }
/** \brief Set the voxel size for the voxel grid
* \note Used only in the VOXEL_GRID_DILATION upsampling method
* \param[in] voxel_size the edge length of a cubic voxel in the voxel grid
*/
inline void
setDilationVoxelSize (float voxel_size) { voxel_size_ = voxel_size; }
/** \brief Get the voxel size for the voxel grid
* \note Used only in the VOXEL_GRID_DILATION upsampling method
*/
inline float
getDilationVoxelSize () const { return (voxel_size_); }
/** \brief Set the number of dilation steps of the voxel grid
* \note Used only in the VOXEL_GRID_DILATION upsampling method
* \param[in] iterations the number of dilation iterations
*/
inline void
setDilationIterations (int iterations) { dilation_iteration_num_ = iterations; }
/** \brief Get the number of dilation steps of the voxel grid
* \note Used only in the VOXEL_GRID_DILATION upsampling method
*/
inline int
getDilationIterations () const { return (dilation_iteration_num_); }
/** \brief Set whether the mls results should be stored for each point in the input cloud
* \param[in] True if the mls results should be stored, otherwise false.
* \note The cache_mls_results_ is forced to true when using upsampling method VOXEL_GRID_DILATION or DISTINCT_CLOUD.
* \note If memory consumption is a concern set to false when not using upsampling method VOXEL_GRID_DILATION or DISTINCT_CLOUD.
*/
inline void
setCacheMLSResults (bool cache_mls_results) { cache_mls_results_ = cache_mls_results; }
/** \brief Get the cache_mls_results_ value (True if the mls results should be stored, otherwise false). */
inline bool
getCacheMLSResults () const { return (cache_mls_results_); }
/** \brief Set the method to be used when projection the point on to the MLS surface.
* \param method
* \note This is only used when polynomial fit is enabled.
*/
inline void
setProjectionMethod (MLSResult::ProjectionMethod method) { projection_method_ = method; }
/** \brief Get the current projection method being used. */
inline MLSResult::ProjectionMethod
getProjectionMethod () const { return (projection_method_); }
/** \brief Get the MLSResults for input cloud
* \note The results are only stored if setCacheMLSResults(true) was called or when using the upsampling method DISTINCT_CLOUD or VOXEL_GRID_DILATION.
* \note This vector is align with the input cloud indices, so use getCorrespondingIndices to get the correct results when using output cloud indices.
*/
inline const std::vector<MLSResult>&
getMLSResults () const { return (mls_results_); }
/** \brief Set the maximum number of threads to use
* \param threads the maximum number of hardware threads to use (0 sets the value to 1)
*/
inline void
setNumberOfThreads (unsigned int threads = 1)
{
threads_ = threads;
}
/** \brief Base method for surface reconstruction for all points given in <setInputCloud (), setIndices ()>
* \param[out] output the resultant reconstructed surface model
*/
void
process (PointCloudOut &output);
/** \brief Get the set of indices with each point in output having the
* corresponding point in input */
inline PointIndicesPtr
getCorrespondingIndices () const { return (corresponding_input_indices_); }
protected:
/** \brief The point cloud that will hold the estimated normals, if set. */
NormalCloudPtr normals_;
/** \brief The distinct point cloud that will be projected to the MLS surface. */
PointCloudInConstPtr distinct_cloud_;
/** \brief The search method template for indices. */
SearchMethod search_method_;
/** \brief A pointer to the spatial search object. */
KdTreePtr tree_;
/** \brief The order of the polynomial to be fit. */
int order_;
/** \brief The nearest neighbors search radius for each point. */
double search_radius_;
/** \brief Parameter for distance based weighting of neighbors (search_radius_ * search_radius_ works fine) */
double sqr_gauss_param_;
/** \brief Parameter that specifies whether the normals should be computed for the input cloud or not */
bool compute_normals_;
/** \brief Parameter that specifies the upsampling method to be used */
UpsamplingMethod upsample_method_;
/** \brief Radius of the circle in the local point plane that will be sampled
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
*/
double upsampling_radius_;
/** \brief Step size for the local plane sampling
* \note Used only in the case of SAMPLE_LOCAL_PLANE upsampling
*/
double upsampling_step_;
/** \brief Parameter that specifies the desired number of points within the search radius
* \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
*/
int desired_num_points_in_radius_;
/** \brief True if the mls results for the input cloud should be stored
* \note This is forced to true when using upsampling methods VOXEL_GRID_DILATION or DISTINCT_CLOUD.
*/
bool cache_mls_results_;
/** \brief Stores the MLS result for each point in the input cloud
* \note Used only in the case of VOXEL_GRID_DILATION or DISTINCT_CLOUD upsampling
*/
std::vector<MLSResult> mls_results_;
/** \brief Parameter that specifies the projection method to be used. */
MLSResult::ProjectionMethod projection_method_;
/** \brief The maximum number of threads the scheduler should use. */
unsigned int threads_;
/** \brief A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling
* \note Used only in the case of VOXEL_GRID_DILATION upsampling
*/
class MLSVoxelGrid
{
public:
struct Leaf { Leaf () : valid (true) {} bool valid; };
MLSVoxelGrid (PointCloudInConstPtr& cloud,
IndicesPtr &indices,
float voxel_size);
void
dilate ();
inline void
getIndexIn1D (const Eigen::Vector3i &index, uint64_t &index_1d) const
{
index_1d = index[0] * data_size_ * data_size_ +
index[1] * data_size_ + index[2];
}
inline void
getIndexIn3D (uint64_t index_1d, Eigen::Vector3i& index_3d) const
{
index_3d[0] = static_cast<Eigen::Vector3i::Scalar> (index_1d / (data_size_ * data_size_));
index_1d -= index_3d[0] * data_size_ * data_size_;
index_3d[1] = static_cast<Eigen::Vector3i::Scalar> (index_1d / data_size_);
index_1d -= index_3d[1] * data_size_;
index_3d[2] = static_cast<Eigen::Vector3i::Scalar> (index_1d);
}
inline void
getCellIndex (const Eigen::Vector3f &p, Eigen::Vector3i& index) const
{
for (int i = 0; i < 3; ++i)
index[i] = static_cast<Eigen::Vector3i::Scalar> ((p[i] - bounding_min_ (i)) / voxel_size_);
}
inline void
getPosition (const uint64_t &index_1d, Eigen::Vector3f &point) const
{
Eigen::Vector3i index_3d;
getIndexIn3D (index_1d, index_3d);
for (int i = 0; i < 3; ++i)
point[i] = static_cast<Eigen::Vector3f::Scalar> (index_3d[i]) * voxel_size_ + bounding_min_[i];
}
typedef std::map<uint64_t, Leaf> HashMap;
HashMap voxel_grid_;
Eigen::Vector4f bounding_min_, bounding_max_;
uint64_t data_size_;
float voxel_size_;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
/** \brief Voxel size for the VOXEL_GRID_DILATION upsampling method */
float voxel_size_;
/** \brief Number of dilation steps for the VOXEL_GRID_DILATION upsampling method */
int dilation_iteration_num_;
/** \brief Number of coefficients, to be computed from the requested order.*/
int nr_coeff_;
/** \brief Collects for each point in output the corrseponding point in the input. */
PointIndicesPtr corresponding_input_indices_;
/** \brief Search for the closest nearest neighbors of a given point using a radius search
* \param[in] index the index of the query point
* \param[out] indices the resultant vector of indices representing the k-nearest neighbors
* \param[out] sqr_distances the resultant squared distances from the query point to the k-nearest neighbors
*/
inline int
searchForNeighbors (int index, std::vector<int> &indices, std::vector<float> &sqr_distances) const
{
return (search_method_ (index, search_radius_, indices, sqr_distances));
}
/** \brief Smooth a given point and its neighborghood using Moving Least Squares.
* \param[in] index the index of the query point in the input cloud
* \param[in] nn_indices the set of nearest neighbors indices for pt
* \param[out] projected_points the set of points projected points around the query point
* (in the case of upsampling method NONE, only the query point projected to its own fitted surface will be returned,
* in the case of the other upsampling methods, multiple points will be returned)
* \param[out] projected_points_normals the normals corresponding to the projected points
* \param[out] corresponding_input_indices the set of indices with each point in output having the corresponding point in input
* \param[out] mls_result stores the MLS result for each point in the input cloud
* (used only in the case of VOXEL_GRID_DILATION or DISTINCT_CLOUD upsampling)
*/
void
computeMLSPointNormal (int index,
const std::vector<int> &nn_indices,
PointCloudOut &projected_points,
NormalCloud &projected_points_normals,
PointIndices &corresponding_input_indices,
MLSResult &mls_result) const;
/** \brief This is a helper function for add projected points
* \param[in] index the index of the query point in the input cloud
* \param[in] point the projected point to be added
* \param[in] normal the projected point's normal to be added
* \param[in] curvature the projected point's curvature
* \param[out] projected_points the set of projected points around the query point
* \param[out] projected_points_normals the normals corresponding to the projected points
* \param[out] corresponding_input_indices the set of indices with each point in output having the corresponding point in input
*/
void
addProjectedPointNormal (int index,
const Eigen::Vector3d &point,
const Eigen::Vector3d &normal,
double curvature,
PointCloudOut &projected_points,
NormalCloud &projected_points_normals,
PointIndices &corresponding_input_indices) const;
void
copyMissingFields (const PointInT &point_in,
PointOutT &point_out) const;
/** \brief Abstract surface reconstruction method.
* \param[out] output the result of the reconstruction
*/
virtual void
performProcessing (PointCloudOut &output);
/** \brief Perform upsampling for the distinct-cloud and voxel-grid methods
* \param[out] output the result of the reconstruction
*/
void
performUpsampling (PointCloudOut &output);
private:
/** \brief Boost-based random number generator algorithm. */
boost::mt19937 rng_alg_;
/** \brief Random number generator using an uniform distribution of floats
* \note Used only in the case of RANDOM_UNIFORM_DENSITY upsampling
*/
boost::shared_ptr<boost::variate_generator<boost::mt19937&,
boost::uniform_real<float> >
> rng_uniform_distribution_;
/** \brief Abstract class get name method. */
std::string
getClassName () const { return ("MovingLeastSquares"); }
};
/** \brief MovingLeastSquaresOMP implementation has been merged into MovingLeastSquares for better maintainability.
* \note Keeping this empty child class for backwards compatibility.
* \author Robert Huitl
* \ingroup surface
*/
template <typename PointInT, typename PointOutT>
class MovingLeastSquaresOMP : public MovingLeastSquares<PointInT, PointOutT>
{
public:
/** \brief Constructor for parallelized Moving Least Squares
* \param threads the maximum number of hardware threads to use (0 sets the value to 1)
*/
MovingLeastSquaresOMP (unsigned int threads = 1)
{
this->setNumberOfThreads (threads);
}
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/surface/impl/mls.hpp>
#endif
#endif /* #ifndef PCL_MLS_H_ */