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sac_model_plane.hpp
/*
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/common/centroid.h>
#include <pcl/common/eigen.h>
#include <pcl/common/concatenate.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::isSampleGood (const Indices &samples) const
{
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
return (false);
}
// Check if the sample points are collinear
// Similar checks are implemented as precaution in computeModelCoefficients,
// so if you find the need to fix something in here, look there, too.
pcl::Vector3fMapConst p0 = (*input_)[samples[0]].getVector3fMap ();
pcl::Vector3fMapConst p1 = (*input_)[samples[1]].getVector3fMap ();
pcl::Vector3fMapConst p2 = (*input_)[samples[2]].getVector3fMap ();
// Check if the norm of the cross-product would be non-zero, otherwise
// normalization will fail. One could also interpret this as kind of check
// if the triangle spanned by those three points would have an area greater
// than zero.
if ((p1 - p0).cross(p2 - p0).stableNorm() < Eigen::NumTraits<float>::dummy_precision ())
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::isSampleGood] Sample points too similar or collinear!\n");
return (false);
}
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::computeModelCoefficients (
const Indices &samples, Eigen::VectorXf &model_coefficients) const
{
// The checks are redundant with isSampleGood above, but since most of the
// computed values are also used to compute the model coefficients, this might
// be a situation where this duplication is acceptable.
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
pcl::Vector3fMapConst p0 = (*input_)[samples[0]].getVector3fMap ();
pcl::Vector3fMapConst p1 = (*input_)[samples[1]].getVector3fMap ();
pcl::Vector3fMapConst p2 = (*input_)[samples[2]].getVector3fMap ();
const Eigen::Vector3f cross = (p1 - p0).cross(p2 - p0);
const float crossNorm = cross.stableNorm();
// Checking for collinearity here
if (crossNorm < Eigen::NumTraits<float>::dummy_precision ())
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Chosen samples are collinear!\n");
return (false);
}
// Compute the plane coefficients from the 3 given points in a straightforward manner
// calculate the plane normal n = (p2-p1) x (p3-p1) = cross (p2-p1, p3-p1)
model_coefficients.resize (model_size_);
model_coefficients.template head<3>() = cross / crossNorm;
// ... + d = 0
model_coefficients[3] = -1.0f * (model_coefficients.template head<3>().dot (p0));
PCL_DEBUG ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Model is (%g,%g,%g,%g).\n",
model_coefficients[0], model_coefficients[1], model_coefficients[2], model_coefficients[3]);
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::getDistancesToModel] Given model is invalid!\n");
return;
}
distances.resize (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the plane
for (std::size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
/*distances[i] = std::abs (model_coefficients[0] * (*input_)[(*indices_)[i]].x +
model_coefficients[1] * (*input_)[(*indices_)[i]].y +
model_coefficients[2] * (*input_)[(*indices_)[i]].z +
model_coefficients[3]);*/
Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
(*input_)[(*indices_)[i]].y,
(*input_)[(*indices_)[i]].z,
1.0f);
distances[i] = std::abs (model_coefficients.dot (pt));
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::selectWithinDistance] Given model is invalid!\n");
return;
}
inliers.clear ();
error_sqr_dists_.clear ();
inliers.reserve (indices_->size ());
error_sqr_dists_.reserve (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the plane
for (std::size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
(*input_)[(*indices_)[i]].y,
(*input_)[(*indices_)[i]].z,
1.0f);
float distance = std::abs (model_coefficients.dot (pt));
if (distance < threshold)
{
// Returns the indices of the points whose distances are smaller than the threshold
inliers.push_back ((*indices_)[i]);
error_sqr_dists_.push_back (static_cast<double> (distance));
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelPlane<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::countWithinDistance] Given model is invalid!\n");
return (0);
}
#if defined (__AVX__) && defined (__AVX2__)
return countWithinDistanceAVX (model_coefficients, threshold);
#elif defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
return countWithinDistanceSSE (model_coefficients, threshold);
#else
return countWithinDistanceStandard (model_coefficients, threshold);
#endif
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelPlane<PointT>::countWithinDistanceStandard (
const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
{
std::size_t nr_p = 0;
// Iterate through the 3d points and calculate the distances from them to the plane
for (; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
(*input_)[(*indices_)[i]].y,
(*input_)[(*indices_)[i]].z,
1.0f);
if (std::abs (model_coefficients.dot (pt)) < threshold)
{
nr_p++;
}
}
return (nr_p);
}
//////////////////////////////////////////////////////////////////////////
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
template <typename PointT> std::size_t
pcl::SampleConsensusModelPlane<PointT>::countWithinDistanceSSE (
const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
{
std::size_t nr_p = 0;
const __m128 a_vec = _mm_set1_ps (model_coefficients[0]);
const __m128 b_vec = _mm_set1_ps (model_coefficients[1]);
const __m128 c_vec = _mm_set1_ps (model_coefficients[2]);
const __m128 d_vec = _mm_set1_ps (model_coefficients[3]);
const __m128 threshold_vec = _mm_set1_ps (threshold);
const __m128 abs_help = _mm_set1_ps (-0.0F); // -0.0F (negative zero) means that all bits are 0, only the sign bit is 1
__m128i res = _mm_set1_epi32(0); // This corresponds to nr_p: 4 32bit integers that, summed together, hold the number of inliers
for (; (i + 4) <= indices_->size (); i += 4)
{
const __m128 mask = _mm_cmplt_ps (dist4 (i, a_vec, b_vec, c_vec, d_vec, abs_help), threshold_vec); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
res = _mm_add_epi32 (res, _mm_and_si128 (_mm_set1_epi32 (1), _mm_castps_si128 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
//const int res = _mm_movemask_ps (mask);
//if (res & 1) nr_p++;
//if (res & 2) nr_p++;
//if (res & 4) nr_p++;
//if (res & 8) nr_p++;
}
nr_p += _mm_extract_epi32 (res, 0);
nr_p += _mm_extract_epi32 (res, 1);
nr_p += _mm_extract_epi32 (res, 2);
nr_p += _mm_extract_epi32 (res, 3);
// Process the remaining points (at most 3)
nr_p += countWithinDistanceStandard(model_coefficients, threshold, i);
return (nr_p);
}
#endif
//////////////////////////////////////////////////////////////////////////
#if defined (__AVX__) && defined (__AVX2__)
template <typename PointT> std::size_t
pcl::SampleConsensusModelPlane<PointT>::countWithinDistanceAVX (
const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
{
std::size_t nr_p = 0;
const __m256 a_vec = _mm256_set1_ps (model_coefficients[0]);
const __m256 b_vec = _mm256_set1_ps (model_coefficients[1]);
const __m256 c_vec = _mm256_set1_ps (model_coefficients[2]);
const __m256 d_vec = _mm256_set1_ps (model_coefficients[3]);
const __m256 threshold_vec = _mm256_set1_ps (threshold);
const __m256 abs_help = _mm256_set1_ps (-0.0F); // -0.0F (negative zero) means that all bits are 0, only the sign bit is 1
__m256i res = _mm256_set1_epi32(0); // This corresponds to nr_p: 8 32bit integers that, summed together, hold the number of inliers
for (; (i + 8) <= indices_->size (); i += 8)
{
const __m256 mask = _mm256_cmp_ps (dist8 (i, a_vec, b_vec, c_vec, d_vec, abs_help), threshold_vec, _CMP_LT_OQ); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
res = _mm256_add_epi32 (res, _mm256_and_si256 (_mm256_set1_epi32 (1), _mm256_castps_si256 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
//const int res = _mm256_movemask_ps (mask);
//if (res & 1) nr_p++;
//if (res & 2) nr_p++;
//if (res & 4) nr_p++;
//if (res & 8) nr_p++;
//if (res & 16) nr_p++;
//if (res & 32) nr_p++;
//if (res & 64) nr_p++;
//if (res & 128) nr_p++;
}
nr_p += _mm256_extract_epi32 (res, 0);
nr_p += _mm256_extract_epi32 (res, 1);
nr_p += _mm256_extract_epi32 (res, 2);
nr_p += _mm256_extract_epi32 (res, 3);
nr_p += _mm256_extract_epi32 (res, 4);
nr_p += _mm256_extract_epi32 (res, 5);
nr_p += _mm256_extract_epi32 (res, 6);
nr_p += _mm256_extract_epi32 (res, 7);
// Process the remaining points (at most 7)
nr_p += countWithinDistanceStandard(model_coefficients, threshold, i);
return (nr_p);
}
#endif
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::optimizeModelCoefficients (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Given model is invalid!\n");
optimized_coefficients = model_coefficients;
return;
}
// Need more than the minimum sample size to make a difference
if (inliers.size () <= sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Not enough inliers found to optimize model coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
optimized_coefficients = model_coefficients;
return;
}
Eigen::Vector4f plane_parameters;
// Use Least-Squares to fit the plane through all the given sample points and find out its coefficients
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
Eigen::Vector4f xyz_centroid;
if (0 == computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, xyz_centroid))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] computeMeanAndCovarianceMatrix failed (returned 0) because there are no valid inliers.\n");
optimized_coefficients = model_coefficients;
return;
}
// Compute the model coefficients
EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
// Hessian form (D = nc . p_plane (centroid here) + p)
optimized_coefficients.resize (model_size_);
optimized_coefficients[0] = eigen_vector [0];
optimized_coefficients[1] = eigen_vector [1];
optimized_coefficients[2] = eigen_vector [2];
optimized_coefficients[3] = 0.0f;
optimized_coefficients[3] = -1.0f * optimized_coefficients.dot (xyz_centroid);
// Make sure it results in a valid model
if (!isModelValid (optimized_coefficients))
{
optimized_coefficients = model_coefficients;
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::projectPoints (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::projectPoints] Given model is invalid!\n");
return;
}
projected_points.header = input_->header;
projected_points.is_dense = input_->is_dense;
Eigen::Vector4f mc (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
// normalize the vector perpendicular to the plane...
mc.normalize ();
// ... and store the resulting normal as a local copy of the model coefficients
Eigen::Vector4f tmp_mc = model_coefficients;
tmp_mc[0] = mc[0];
tmp_mc[1] = mc[1];
tmp_mc[2] = mc[2];
// Copy all the data fields from the input cloud to the projected one?
if (copy_data_fields)
{
// Allocate enough space and copy the basics
projected_points.resize (input_->size ());
projected_points.width = input_->width;
projected_points.height = input_->height;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < input_->size (); ++i)
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
// Iterate through the 3d points and calculate the distances from them to the plane
for (const auto &inlier : inliers)
{
// Calculate the distance from the point to the plane
Eigen::Vector4f p ((*input_)[inlier].x,
(*input_)[inlier].y,
(*input_)[inlier].z,
1);
// use normalized coefficients to calculate the scalar projection
float distance_to_plane = tmp_mc.dot (p);
pcl::Vector4fMap pp = projected_points[inlier].getVector4fMap ();
pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
}
}
else
{
// Allocate enough space and copy the basics
projected_points.resize (inliers.size ());
projected_points.width = inliers.size ();
projected_points.height = 1;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < inliers.size (); ++i)
{
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
}
// Iterate through the 3d points and calculate the distances from them to the plane
for (std::size_t i = 0; i < inliers.size (); ++i)
{
// Calculate the distance from the point to the plane
Eigen::Vector4f p ((*input_)[inliers[i]].x,
(*input_)[inliers[i]].y,
(*input_)[inliers[i]].z,
1.0f);
// use normalized coefficients to calculate the scalar projection
float distance_to_plane = tmp_mc.dot (p);
pcl::Vector4fMap pp = projected_points[i].getVector4fMap ();
pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::doSamplesVerifyModel (
const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::doSamplesVerifyModel] Given model is invalid!\n");
return (false);
}
for (const auto &index : indices)
{
Eigen::Vector4f pt ((*input_)[index].x,
(*input_)[index].y,
(*input_)[index].z,
1.0f);
if (std::abs (model_coefficients.dot (pt)) > threshold)
{
return (false);
}
}
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelPlane(T) template class PCL_EXPORTS pcl::SampleConsensusModelPlane<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
centroid.h
Define methods for centroid estimation and covariance matrix calculus.
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition sac_model_plane.h:143
pcl::SampleConsensusModelPlane::PointCloud
typename SampleConsensusModel< PointT >::PointCloud PointCloud
Definition sac_model_plane.h:151
pcl::SampleConsensusModelPlane::optimizeModelCoefficients
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the plane coefficients using the given inlier set and return them to the user.
Definition sac_model_plane.hpp:312
pcl::SampleConsensusModelPlane::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given plane model.
Definition sac_model_plane.hpp:122
pcl::SampleConsensusModelPlane::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given plane model coefficients.
Definition sac_model_plane.hpp:452
pcl::SampleConsensusModelPlane::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
Definition sac_model_plane.hpp:153
pcl::SampleConsensusModelPlane::projectPoints
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the plane model.
Definition sac_model_plane.hpp:366
pcl::SampleConsensusModelPlane::computeModelCoefficients
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid plane model, compute the model coefficients fr...
Definition sac_model_plane.hpp:81
pcl::SampleConsensusModelPlane::countWithinDistanceStandard
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
Definition sac_model_plane.hpp:211
pcl::SampleConsensusModelPlane::countWithinDistance
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
Definition sac_model_plane.hpp:191
pcl::computeMeanAndCovarianceMatrix
unsigned int computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition centroid.hpp:509
pcl::eigen33
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition eigen.hpp:295
pcl::device::cross
__device__ __host__ __forceinline__ float3 cross(const float3 &v1, const float3 &v2)
Definition utils.hpp:107
pcl::Vector4fMap
Eigen::Map< Eigen::Vector4f, Eigen::Aligned > Vector4fMap
Definition point_types.hpp:201
pcl::Vector3fMapConst
const Eigen::Map< const Eigen::Vector3f > Vector3fMapConst
Definition point_types.hpp:200
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133