template<typename PointInT, typename PointNT>
class pcl::FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf >
FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals.
- If you use this code in any academic work, please cite:
- R.B. Rusu, N. Blodow, M. Beetz. Fast Point Feature Histograms (FPFH) for 3D Registration. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, May 12-17 2009.
- R.B. Rusu, A. Holzbach, N. Blodow, M. Beetz. Fast Geometric Point Labeling using Conditional Random Fields. In Proceedings of the 22nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, October 11-15 2009.
- The convention for FPFH features is:
- if a query point's nearest neighbors cannot be estimated, the FPFH feature will be set to NaN (not a number)
- it is impossible to estimate a FPFH descriptor for a point that doesn't have finite 3D coordinates. Therefore, any point that contains NaN data on x, y, or z, will have its FPFH feature property set to NaN.
- The code is stateful as we do not expect this class to be multicore parallelized. Please look at FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
- Radu B. Rusu
Definition at line 254 of file fpfh.h.
template<typename PointInT , typename PointNT >
Estimate the Fast Point Feature Histograms (FPFH) descriptors at a set of points given by <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()
|output||the resultant point cloud model dataset that contains the FPFH feature estimates |
Reimplemented from pcl::FPFHEstimation< PointInT, PointNT, pcl::FPFHSignature33 >.
Definition at line 304 of file fpfh.hpp.