template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
// For all points
for (size_t cp = 0; cp < indices_->size (); ++cp)
{
// Get the initial estimates of point positions and their neighborhoods
if (!searchForNeighbors (int (cp), nn_indices, nn_sqr_dists))
continue;
// Check the number of nearest neighbors for normal estimation (and later
// for polynomial fit as well)
if (nn_indices.size () < 3)
continue;
PointCloudOut projected_points;
NormalCloud projected_points_normals;
// Get a plane approximating the local surface's tangent and project point onto it
computeMLSPointNormal (int (cp), *input_, nn_indices, nn_sqr_dists, projected_points, projected_points_normals);
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
BOOST_FOREACH (typename MLSVoxelGrid::HashMap::value_type voxel, voxel_grid.voxel_grid_)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (voxel.first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = p.getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
float d_before = (pos - input_point).norm (),
d_after = (result_point.getVector3fMap () - input_point). norm();
if (d_after > d_before)
continue;
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performUpsampling (PointCloudOut &output)
{
if (upsample_method_ == DISTINCT_CLOUD)
{
for (size_t dp_i = 0; dp_i < distinct_cloud_->size (); ++dp_i) // dp_i = distinct_point_i
{
// Distinct cloud may have nan points, skip them
if (!pcl_isfinite (distinct_cloud_->points[dp_i].x))
continue;
// Get 3D position of point
//Eigen::Vector3f pos = distinct_cloud_->points[dp_i].getVector3fMap ();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (distinct_cloud_->points[dp_i], 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = distinct_cloud_->points[dp_i].getVector3fMap ().template cast<double> ();
float u_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].u_axis)),
v_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].v_axis));
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u_axis, mls_results_[input_index].v_axis,
mls_results_[input_index].plane_normal,
mls_results_[input_index].mean,
mls_results_[input_index].curvature,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
// Copy additional point information if available
copyMissingFields (input_->points[input_index], result_point);
// Store the id of the original point
corresponding_input_indices_->indices.push_back (input_index);
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
for (typename MLSVoxelGrid::HashMap::iterator m_it = voxel_grid.voxel_grid_.begin (); m_it != voxel_grid.voxel_grid_.end (); ++m_it)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (m_it->first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3d add_point = p.getVector3fMap ().template cast<double> ();
float u_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].u_axis)),
v_disp = static_cast<float> ((add_point - mls_results_[input_index].mean).dot (mls_results_[input_index].v_axis));
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u_axis, mls_results_[input_index].v_axis,
mls_results_[input_index].plane_normal,
mls_results_[input_index].mean,
mls_results_[input_index].curvature,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
// Copy additional point information if available
copyMissingFields (input_->points[input_index], result_point);
// Store the id of the original point
corresponding_input_indices_->indices.push_back (input_index);
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::computeMLSPointNormal (int index,
const PointCloudIn &input,
const std::vector<int> &nn_indices,
std::vector<float> &nn_sqr_dists,
PointCloudOut &projected_points,
NormalCloud &projected_points_normals)
{
// Compute the plane coefficients
//pcl::computePointNormal<PointInT> (*input_, nn_indices, model_coefficients, curvature);
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
Eigen::Vector4f xyz_centroid;
// Estimate the XYZ centroid
pcl::compute3DCentroid (input, nn_indices, xyz_centroid);
//pcl::compute3DCentroid (input, nn_indices, xyz_centroid);
pcl::computeCovarianceMatrix (input, nn_indices, xyz_centroid, covariance_matrix);
// Compute the 3x3 covariance matrix
EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
Eigen::Vector4f model_coefficients;
pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
model_coefficients.head<3> () = eigen_vector;
model_coefficients[3] = 0;
model_coefficients[3] = -1 * model_coefficients.dot (xyz_centroid);
// Projected query point
Eigen::Vector3f point = input[(*indices_)[index]].getVector3fMap ();
float distance = point.dot (model_coefficients.head<3> ()) + model_coefficients[3];
point -= distance * model_coefficients.head<3> ();
float curvature = covariance_matrix.trace ();
// Compute the curvature surface change
if (curvature != 0)
curvature = fabsf (eigen_value / curvature);
// Get a copy of the plane normal easy access
Eigen::Vector3d plane_normal = model_coefficients.head<3> ().cast<double> ();
// Vector in which the polynomial coefficients will be put
Eigen::VectorXd c_vec;
// Local coordinate system (Darboux frame)
Eigen::Vector3d v (0.0f, 0.0f, 0.0f), u (0.0f, 0.0f, 0.0f);
// Perform polynomial fit to update point and normal
////////////////////////////////////////////////////
if (polynomial_fit_ && static_cast<int> (nn_indices.size ()) >= nr_coeff_)
{
// Update neighborhood, since point was projected, and computing relative
// positions. Note updating only distances for the weights for speed
std::vector<Eigen::Vector3d> de_meaned (nn_indices.size ());
for (size_t ni = 0; ni < nn_indices.size (); ++ni)
{
de_meaned[ni][0] = input_->points[nn_indices[ni]].x - point[0];
de_meaned[ni][1] = input_->points[nn_indices[ni]].y - point[1];
de_meaned[ni][2] = input_->points[nn_indices[ni]].z - point[2];
nn_sqr_dists[ni] = static_cast<float> (de_meaned[ni].dot (de_meaned[ni]));
}
// Allocate matrices and vectors to hold the data used for the polynomial fit
Eigen::VectorXd weight_vec (nn_indices.size ());
Eigen::MatrixXd P (nr_coeff_, nn_indices.size ());
Eigen::VectorXd f_vec (nn_indices.size ());
Eigen::MatrixXd P_weight; // size will be (nr_coeff_, nn_indices.size ());
Eigen::MatrixXd P_weight_Pt (nr_coeff_, nr_coeff_);
// Get local coordinate system (Darboux frame)
v = plane_normal.unitOrthogonal ();
u = plane_normal.cross (v);
// Go through neighbors, transform them in the local coordinate system,
// save height and the evaluation of the polynome's terms
double u_coord, v_coord, u_pow, v_pow;
for (size_t ni = 0; ni < nn_indices.size (); ++ni)
{
// (re-)compute weights
weight_vec (ni) = exp (-nn_sqr_dists[ni] / sqr_gauss_param_);
// transforming coordinates
u_coord = de_meaned[ni].dot (u);
v_coord = de_meaned[ni].dot (v);
f_vec (ni) = de_meaned[ni].dot (plane_normal);
// compute the polynomial's terms at the current point
int j = 0;
u_pow = 1;
for (int ui = 0; ui <= order_; ++ui)
{
v_pow = 1;
for (int vi = 0; vi <= order_ - ui; ++vi)
{
P (j++, ni) = u_pow * v_pow;
v_pow *= v_coord;
}
u_pow *= u_coord;
}
}
// Computing coefficients
P_weight = P * weight_vec.asDiagonal ();
P_weight_Pt = P_weight * P.transpose ();
c_vec = P_weight * f_vec;
P_weight_Pt.llt ().solveInPlace (c_vec);
}
switch (upsample_method_)
{
case (NONE):
{
Eigen::Vector3d normal = plane_normal;
if (polynomial_fit_ && static_cast<int> (nn_indices.size ()) >= nr_coeff_ && pcl_isfinite (c_vec[0]))
{
point += (c_vec[0] * plane_normal).cast<float> ();
// Compute tangent vectors using the partial derivates evaluated at (0,0) which is c_vec[order_+1] and c_vec[1]
if (compute_normals_)
normal = plane_normal - c_vec[order_ + 1] * u - c_vec[1] * v;
}
PointOutT aux;
aux.x = point[0];
aux.y = point[1];
aux.z = point[2];
projected_points.push_back (aux);
if (compute_normals_)
{
pcl::Normal aux_normal;
aux_normal.normal_x = static_cast<float> (normal[0]);
aux_normal.normal_y = static_cast<float> (normal[1]);
aux_normal.normal_z = static_cast<float> (normal[2]);
aux_normal.curvature = curvature;
projected_points_normals.push_back (aux_normal);
}
break;
}
case (SAMPLE_LOCAL_PLANE):
{
// Uniformly sample a circle around the query point using the radius and step parameters
for (float u_disp = -static_cast<float> (upsampling_radius_); u_disp <= upsampling_radius_; u_disp += static_cast<float> (upsampling_step_))
for (float v_disp = -static_cast<float> (upsampling_radius_); v_disp <= upsampling_radius_; v_disp += static_cast<float> (upsampling_step_))
if (u_disp*u_disp + v_disp*v_disp < upsampling_radius_*upsampling_radius_)
{
PointOutT projected_point;
pcl::Normal projected_normal;
projectPointToMLSSurface (u_disp, v_disp, u, v, plane_normal, curvature, point, c_vec,
static_cast<int> (nn_indices.size ()),
projected_point, projected_normal);
projected_points.push_back (projected_point);
if (compute_normals_)
projected_points_normals.push_back (projected_normal);
}
break;
}
case (RANDOM_UNIFORM_DENSITY):
{
// Compute the local point density and add more samples if necessary
int num_points_to_add = static_cast<int> (floor (desired_num_points_in_radius_ / 2.0 / static_cast<double> (nn_indices.size ())));
// Just add the query point, because the density is good
if (num_points_to_add <= 0)
{
// Just add the current point
Eigen::Vector3d normal = plane_normal;
if (polynomial_fit_ && static_cast<int> (nn_indices.size ()) >= nr_coeff_ && pcl_isfinite (c_vec[0]))
{
// Projection onto MLS surface along Darboux normal to the height at (0,0)
point += (c_vec[0] * plane_normal).cast<float> ();
// Compute tangent vectors using the partial derivates evaluated at (0,0) which is c_vec[order_+1] and c_vec[1]
if (compute_normals_)
normal = plane_normal - c_vec[order_ + 1] * u - c_vec[1] * v;
}
PointOutT aux;
aux.x = point[0];
aux.y = point[1];
aux.z = point[2];
projected_points.push_back (aux);
if (compute_normals_)
{
pcl::Normal aux_normal;
aux_normal.normal_x = static_cast<float> (normal[0]);
aux_normal.normal_y = static_cast<float> (normal[1]);
aux_normal.normal_z = static_cast<float> (normal[2]);
aux_normal.curvature = curvature;
projected_points_normals.push_back (aux_normal);
}
}
else
{
// Sample the local plane
for (int num_added = 0; num_added < num_points_to_add;)
{
float u_disp = (*rng_uniform_distribution_) (),
v_disp = (*rng_uniform_distribution_) ();
// Check if inside circle; if not, try another coin flip
if (u_disp * u_disp + v_disp * v_disp > search_radius_ * search_radius_/4)
continue;
PointOutT projected_point;
pcl::Normal projected_normal;
projectPointToMLSSurface (u_disp, v_disp, u, v, plane_normal, curvature, point, c_vec,
static_cast<int> (nn_indices.size ()),
projected_point, projected_normal);
projected_points.push_back (projected_point);
if (compute_normals_)
projected_points_normals.push_back (projected_normal);
num_added ++;
}
}
break;
}
case (VOXEL_GRID_DILATION):
{
// Take all point pairs and sample space between them in a grid-fashion
// \note consider only point pairs with increasing indices
MLSResult result (plane_normal, u, v, c_vec, static_cast<int> (nn_indices.size ()), curvature);
mls_results_[index] = result;
break;
}
}
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
typedef typename pcl::traits::fieldList<typename PointCloudIn::PointType>::type FieldListInput;
typedef typename pcl::traits::fieldList<typename PointCloudOut::PointType>::type FieldListOutput;
// For all points
for (size_t cp = 0; cp < indices_->size (); ++cp)
{
// Get the initial estimates of point positions and their neighborhoods
if (!searchForNeighbors (int (cp), nn_indices, nn_sqr_dists))
continue;
// Check the number of nearest neighbors for normal estimation (and later
// for polynomial fit as well)
if (nn_indices.size () < 3)
continue;
PointCloudOut projected_points;
NormalCloud projected_points_normals;
// Get a plane approximating the local surface's tangent and project point onto it
computeMLSPointNormal (int (cp), *input_, nn_indices, nn_sqr_dists, projected_points, projected_points_normals);
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[(*indices_)[cp]], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
for (size_t pp = 0; pp < projected_points.size (); ++pp)
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
projected_points.points[pp], "rgb", rgb_input));
}
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
}
if (upsample_method_ == DISTINCT_CLOUD)
{
for (size_t dp_i = 0; dp_i < distinct_cloud_->size (); ++dp_i) // dp_i = distinct_point_i
{
// Distinct cloud may have nan points, skip them
if (!pcl_isfinite (distinct_cloud_->points[dp_i].x))
continue;
// Get 3D position of point
//Eigen::Vector3f pos = distinct_cloud_->points[dp_i].getVector3fMap ();
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (distinct_cloud_->points[dp_i], 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = distinct_cloud_->points[dp_i].getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[input_index], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
result_point, "rgb", rgb_input));
}
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
// For the voxel grid upsampling method, generate the voxel grid and dilate it
// Then, project the newly obtained points to the MLS surface
if (upsample_method_ == VOXEL_GRID_DILATION)
{
MLSVoxelGrid voxel_grid (input_, indices_, voxel_size_);
for (int iteration = 0; iteration < dilation_iteration_num_; ++iteration)
voxel_grid.dilate ();
for (typename MLSVoxelGrid::HashMap::iterator m_it = voxel_grid.voxel_grid_.begin (); m_it != voxel_grid.voxel_grid_.end (); ++m_it)
{
// Get 3D position of point
Eigen::Vector3f pos;
voxel_grid.getPosition (m_it->first, pos);
PointInT p;
p.x = pos[0];
p.y = pos[1];
p.z = pos[2];
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree_->nearestKSearch (p, 1, nn_indices, nn_dists);
int input_index = nn_indices.front ();
// If the closest point did not have a valid MLS fitting result
// OR if it is too far away from the sampled point
if (mls_results_[input_index].valid == false)
continue;
Eigen::Vector3f add_point = p.getVector3fMap (),
input_point = input_->points[input_index].getVector3fMap ();
Eigen::Vector3d aux = mls_results_[input_index].u;
Eigen::Vector3f u = aux.cast<float> ();
aux = mls_results_[input_index].v;
Eigen::Vector3f v = aux.cast<float> ();
float u_disp = (add_point - input_point).dot (u),
v_disp = (add_point - input_point).dot (v);
PointOutT result_point;
pcl::Normal result_normal;
projectPointToMLSSurface (u_disp, v_disp,
mls_results_[input_index].u, mls_results_[input_index].v,
mls_results_[input_index].plane_normal,
mls_results_[input_index].curvature,
input_point,
mls_results_[input_index].c_vec,
mls_results_[input_index].num_neighbors,
result_point, result_normal);
float d_before = (pos - input_point).norm (),
d_after = (result_point.getVector3fMap () - input_point). norm();
if (d_after > d_before)
continue;
/// Copy RGB information if available
float rgb_input;
bool rgb_exists_input;
pcl::for_each_type<FieldListInput> (pcl::CopyIfFieldExists<typename PointCloudIn::PointType, float> (
input_->points[input_index], "rgb", rgb_exists_input, rgb_input));
if (rgb_exists_input)
{
pcl::for_each_type<FieldListOutput> (pcl::SetIfFieldExists<typename PointCloudOut::PointType, float> (
result_point, "rgb", rgb_input));
}
output.push_back (result_point);
if (compute_normals_)
normals_->push_back (result_normal);
}
}
}