void
compute (const pcl::PCLPointCloud2::ConstPtr &input,
pcl::PCLPointCloud2 &output,
double search_radius,
bool sqr_gauss_param_set,
double sqr_gauss_param,
int polynomial_order)
{
PointCloud<PointXYZ>::Ptr xyz_cloud_pre (new pcl::PointCloud<PointXYZ> ()),
xyz_cloud (new pcl::PointCloud<PointXYZ> ());
fromPCLPointCloud2 (*input, *xyz_cloud_pre);
// Filter the NaNs from the cloud
for (size_t i = 0; i < xyz_cloud_pre->size (); ++i)
if (pcl_isfinite (xyz_cloud_pre->points[i].x))
xyz_cloud->push_back (xyz_cloud_pre->points[i]);
xyz_cloud->header = xyz_cloud_pre->header;
xyz_cloud->height = 1;
xyz_cloud->width = static_cast<uint32_t> (xyz_cloud->size ());
xyz_cloud->is_dense = false;
PointCloud<PointNormal>::Ptr xyz_cloud_smoothed (new PointCloud<PointNormal> ());
MovingLeastSquares<PointXYZ, PointNormal> mls;
mls.setInputCloud (xyz_cloud);
mls.setSearchRadius (search_radius);
if (sqr_gauss_param_set) mls.setSqrGaussParam (sqr_gauss_param);
mls.setPolynomialOrder (polynomial_order);
// mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::SAMPLE_LOCAL_PLANE);
// mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::RANDOM_UNIFORM_DENSITY);
// mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::VOXEL_GRID_DILATION);
mls.setUpsamplingMethod (MovingLeastSquares<PointXYZ, PointNormal>::NONE);
mls.setPointDensity (60000 * int (search_radius)); // 300 points in a 5 cm radius
mls.setUpsamplingRadius (0.025);
mls.setUpsamplingStepSize (0.015);
mls.setDilationIterations (2);
mls.setDilationVoxelSize (0.01f);
search::KdTree<PointXYZ>::Ptr tree (new search::KdTree<PointXYZ> ());
mls.setSearchMethod (tree);
mls.setComputeNormals (true);
PCL_INFO ("Computing smoothed surface and normals with search_radius %f , sqr_gaussian_param %f, polynomial order %d\n",
mls.getSearchRadius(), mls.getSqrGaussParam(), mls.getPolynomialOrder());
TicToc tt;
tt.tic ();
mls.process (*xyz_cloud_smoothed);
print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", xyz_cloud_smoothed->width * xyz_cloud_smoothed->height); print_info (" points]\n");
toPCLPointCloud2 (*xyz_cloud_smoothed, output);
}
static void computeNormals(PointCloudPtr pcl_cloud_input, pcl::PointCloud<PointNormal>::Ptr pcl_cloud_normals, const double search_radius)
{
typedef pcl::search::KdTree<PointT> KdTree;
typedef typename KdTree::Ptr KdTreePtr;
// Create a KD-Tree
KdTreePtr tree = boost::make_shared<pcl::search::KdTree<PointT> >();
MovingLeastSquares<PointT, PointNormal> mls;
mls.setComputeNormals(true);
mls.setInputCloud(pcl_cloud_input);
//mls.setPolynomialFit(true);
mls.setSearchMethod(tree);
mls.setSearchRadius(search_radius);
mls.process(*pcl_cloud_normals);
}
