template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::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
{
PointOutT aux;
aux.x = static_cast<float> (point[0]);
aux.y = static_cast<float> (point[1]);
aux.z = static_cast<float> (point[2]);
// Copy additional point information if available
copyMissingFields (input_->points[index], aux);
projected_points.push_back (aux);
corresponding_input_indices.indices.push_back (index);
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);
}
}
template <typename PointInT, typename PointOutT, typename NormalT> void
pcl::TrajkovicKeypoint3D<PointInT, PointOutT, NormalT>::detectKeypoints (PointCloudOut &output)
{
response_.reset (new pcl::PointCloud<float> (input_->width, input_->height));
const Normals &normals = *normals_;
const PointCloudIn &input = *input_;
pcl::PointCloud<float>& response = *response_;
const int w = static_cast<int> (input_->width) - half_window_size_;
const int h = static_cast<int> (input_->height) - half_window_size_;
if (method_ == FOUR_CORNERS)
{
#ifdef _OPENMP
#pragma omp parallel for num_threads (threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT ¢er = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
// Get rid of isolated points
if (!count) continue;
float sn1 = squaredNormalsDiff (up, center);
float sn2 = squaredNormalsDiff (down, center);
float r1 = sn1 + sn2;
float r2 = squaredNormalsDiff (right, center) + squaredNormalsDiff (left, center);
float d = std::min (r1, r2);
if (d < first_threshold_) continue;
sn1 = sqrt (sn1);
sn2 = sqrt (sn2);
float b1 = normalsDiff (right, up) * sn1;
b1+= normalsDiff (left, down) * sn2;
float b2 = normalsDiff (right, down) * sn2;
b2+= normalsDiff (left, up) * sn1;
float B = std::min (b1, b2);
float A = r2 - r1 - 2*B;
response (i,j) = ((B < 0) && ((B + A) > 0)) ? r1 - ((B*B)/A) : d;
}
}
}
else
{
#ifdef _OPENMP
#pragma omp parallel for num_threads (threads_)
#endif
for(int j = half_window_size_; j < h; ++j)
{
for(int i = half_window_size_; i < w; ++i)
{
if (!isFinite (input (i,j))) continue;
const NormalT ¢er = normals (i,j);
if (!isFinite (center)) continue;
int count = 0;
const NormalT &up = getNormalOrNull (i, j-half_window_size_, count);
const NormalT &down = getNormalOrNull (i, j+half_window_size_, count);
const NormalT &left = getNormalOrNull (i-half_window_size_, j, count);
const NormalT &right = getNormalOrNull (i+half_window_size_, j, count);
const NormalT &upleft = getNormalOrNull (i-half_window_size_, j-half_window_size_, count);
const NormalT &upright = getNormalOrNull (i+half_window_size_, j-half_window_size_, count);
const NormalT &downleft = getNormalOrNull (i-half_window_size_, j+half_window_size_, count);
const NormalT &downright = getNormalOrNull (i+half_window_size_, j+half_window_size_, count);
// Get rid of isolated points
if (!count) continue;
std::vector<float> r (4,0);
r[0] = squaredNormalsDiff (up, center);
r[0]+= squaredNormalsDiff (down, center);
r[1] = squaredNormalsDiff (upright, center);
r[1]+= squaredNormalsDiff (downleft, center);
r[2] = squaredNormalsDiff (right, center);
r[2]+= squaredNormalsDiff (left, center);
r[3] = squaredNormalsDiff (downright, center);
r[3]+= squaredNormalsDiff (upleft, center);
float d = *(std::min_element (r.begin (), r.end ()));
if (d < first_threshold_) continue;
std::vector<float> B (4,0);
std::vector<float> A (4,0);
std::vector<float> sumAB (4,0);
B[0] = normalsDiff (upright, up) * normalsDiff (up, center);
B[0]+= normalsDiff (downleft, down) * normalsDiff (down, center);
B[1] = normalsDiff (right, upright) * normalsDiff (upright, center);
B[1]+= normalsDiff (left, downleft) * normalsDiff (downleft, center);
B[2] = normalsDiff (downright, right) * normalsDiff (downright, center);
B[2]+= normalsDiff (upleft, left) * normalsDiff (upleft, center);
B[3] = normalsDiff (down, downright) * normalsDiff (downright, center);
B[3]+= normalsDiff (up, upleft) * normalsDiff (upleft, center);
A[0] = r[1] - r[0] - B[0] - B[0];
A[1] = r[2] - r[1] - B[1] - B[1];
A[2] = r[3] - r[2] - B[2] - B[2];
A[3] = r[0] - r[3] - B[3] - B[3];
sumAB[0] = A[0] + B[0];
sumAB[1] = A[1] + B[1];
sumAB[2] = A[2] + B[2];
sumAB[3] = A[3] + B[3];
if ((*std::max_element (B.begin (), B.end ()) < 0) &&
(*std::min_element (sumAB.begin (), sumAB.end ()) > 0))
{
std::vector<float> D (4,0);
D[0] = B[0] * B[0] / A[0];
D[1] = B[1] * B[1] / A[1];
D[2] = B[2] * B[2] / A[2];
D[3] = B[3] * B[3] / A[3];
response (i,j) = *(std::min (D.begin (), D.end ()));
}
else
response (i,j) = d;
}
}
}
// Non maximas suppression
std::vector<int> indices = *indices_;
std::sort (indices.begin (), indices.end (),
boost::bind (&TrajkovicKeypoint3D::greaterCornernessAtIndices, this, _1, _2));
output.clear ();
output.reserve (input_->size ());
std::vector<bool> occupency_map (indices.size (), false);
const int width (input_->width);
const int height (input_->height);
const int occupency_map_size (indices.size ());
#ifdef _OPENMP
#pragma omp parallel for shared (output) num_threads (threads_)
#endif
for (int i = 0; i < indices.size (); ++i)
{
int idx = indices[i];
if ((response_->points[idx] < second_threshold_) || occupency_map[idx])
continue;
PointOutT p;
p.getVector3fMap () = input_->points[idx].getVector3fMap ();
p.intensity = response_->points [idx];
#ifdef _OPENMP
#pragma omp critical
#endif
{
output.push_back (p);
keypoints_indices_->indices.push_back (idx);
}
const int x = idx % width;
const int y = idx / width;
const int u_end = std::min (width, x + half_window_size_);
const int v_end = std::min (height, y + half_window_size_);
for(int v = std::max (0, y - half_window_size_); v < v_end; ++v)
for(int u = std::max (0, x - half_window_size_); u < u_end; ++u)
occupency_map[v*width + u] = true;
}
output.height = 1;
output.width = static_cast<uint32_t> (output.size());
// we don not change the denseness
output.is_dense = true;
}
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>::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, typename IntensityT> void
pcl::HarrisKeypoint2D<PointInT, PointOutT, IntensityT>::detectKeypoints (PointCloudOut &output)
{
derivatives_cols_.resize (input_->width, input_->height);
derivatives_rows_.resize (input_->width, input_->height);
//Compute cloud intensities first derivatives along columns and rows
//!!! nsallem 20120220 : we don't test here for density so if one term in nan the result is nan
int w = static_cast<int> (input_->width) - 1;
int h = static_cast<int> (input_->height) - 1;
// j = 0 --> j-1 out of range ; use 0
// i = 0 --> i-1 out of range ; use 0
derivatives_cols_(0,0) = (intensity_ ((*input_) (0,1)) - intensity_ ((*input_) (0,0))) * 0.5;
derivatives_rows_(0,0) = (intensity_ ((*input_) (1,0)) - intensity_ ((*input_) (0,0))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int i = 1; i < w; ++i)
{
derivatives_cols_(i,0) = (intensity_ ((*input_) (i,1)) - intensity_ ((*input_) (i,0))) * 0.5;
}
derivatives_rows_(w,0) = (intensity_ ((*input_) (w,0)) - intensity_ ((*input_) (w-1,0))) * 0.5;
derivatives_cols_(w,0) = (intensity_ ((*input_) (w,1)) - intensity_ ((*input_) (w,0))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, derivatives_rows_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int j = 1; j < h; ++j)
{
// i = 0 --> i-1 out of range ; use 0
derivatives_rows_(0,j) = (intensity_ ((*input_) (1,j)) - intensity_ ((*input_) (0,j))) * 0.5;
for(int i = 1; i < w; ++i)
{
// derivative with respect to rows
derivatives_rows_(i,j) = (intensity_ ((*input_) (i+1,j)) - intensity_ ((*input_) (i-1,j))) * 0.5;
// derivative with respect to cols
derivatives_cols_(i,j) = (intensity_ ((*input_) (i,j+1)) - intensity_ ((*input_) (i,j-1))) * 0.5;
}
// i = w --> w+1 out of range ; use w
derivatives_rows_(w,j) = (intensity_ ((*input_) (w,j)) - intensity_ ((*input_) (w-1,j))) * 0.5;
}
// j = h --> j+1 out of range use h
derivatives_cols_(0,h) = (intensity_ ((*input_) (0,h)) - intensity_ ((*input_) (0,h-1))) * 0.5;
derivatives_rows_(0,h) = (intensity_ ((*input_) (1,h)) - intensity_ ((*input_) (0,h))) * 0.5;
// #ifdef _OPENMP
// //#pragma omp parallel for shared (derivatives_cols_, input_) num_threads (threads_)
// #pragma omp parallel for num_threads (threads_)
// #endif
for(int i = 1; i < w; ++i)
{
derivatives_cols_(i,h) = (intensity_ ((*input_) (i,h)) - intensity_ ((*input_) (i,h-1))) * 0.5;
}
derivatives_rows_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w-1,h))) * 0.5;
derivatives_cols_(w,h) = (intensity_ ((*input_) (w,h)) - intensity_ ((*input_) (w,h-1))) * 0.5;
float highest_response_;
switch (method_)
{
case HARRIS:
responseHarris(*response_, highest_response_);
break;
case NOBLE:
responseNoble(*response_, highest_response_);
break;
case LOWE:
responseLowe(*response_, highest_response_);
break;
case TOMASI:
responseTomasi(*response_, highest_response_);
break;
}
if (!nonmax_)
output = *response_;
else
{
threshold_*= highest_response_;
std::sort (indices_->begin (), indices_->end (),
boost::bind (&HarrisKeypoint2D::greaterIntensityAtIndices, this, _1, _2));
output.clear ();
output.reserve (response_->size());
std::vector<bool> occupency_map (response_->size (), false);
int width (response_->width);
int height (response_->height);
const int occupency_map_size (occupency_map.size ());
#ifdef _OPENMP
#pragma omp parallel for shared (output, occupency_map) private (width, height) num_threads(threads_)
#endif
for (int idx = 0; idx < occupency_map_size; ++idx)
{
if (occupency_map[idx] || response_->points [indices_->at (idx)].intensity < threshold_ || !isFinite (response_->points[idx]))
continue;
#ifdef _OPENMP
#pragma omp critical
#endif
output.push_back (response_->at (indices_->at (idx)));
int u_end = std::min (width, indices_->at (idx) % width + min_distance_);
int v_end = std::min (height, indices_->at (idx) / width + min_distance_);
for(int u = std::max (0, indices_->at (idx) % width - min_distance_); u < u_end; ++u)
for(int v = std::max (0, indices_->at (idx) / width - min_distance_); v < v_end; ++v)
occupency_map[v*input_->width+u] = true;
}
// if (refine_)
// refineCorners (output);
output.height = 1;
output.width = static_cast<uint32_t> (output.size());
}
// we don not change the denseness
output.is_dense = input_->is_dense;
}
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>::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);
}
}
}
