sac_model_normal_plane.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_PLANE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_PLANE_H_
#include <pcl/sample_consensus/sac_model_normal_plane.h>
#include <pcl/sample_consensus/impl/sac_model_plane.hpp> // for dist4, dist8
#include <pcl/common/common.h> // for getAngle3D
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT> void
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
if (!normals_)
{
PCL_ERROR ("[pcl::SampleConsensusModelNormalPlane::selectWithinDistance] No input dataset containing normals was given!\n");
inliers.clear ();
return;
}
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
{
inliers.clear ();
return;
}
// Obtain the plane normal
Eigen::Vector4f coeff = model_coefficients;
coeff[3] = 0.0f;
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)
{
const PointT &pt = (*input_)[(*indices_)[i]];
const PointNT &nt = (*normals_)[(*indices_)[i]];
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f p (pt.x, pt.y, pt.z, 0.0f);
Eigen::Vector4f n (nt.normal_x, nt.normal_y, nt.normal_z, 0.0f);
double d_euclid = std::abs (coeff.dot (p) + model_coefficients[3]);
// Calculate the angular distance between the point normal and the plane normal
double d_normal = std::abs (getAngle3D (n, coeff));
d_normal = (std::min) (d_normal, M_PI - d_normal);
// Weight with the point curvature. On flat surfaces, curvature -> 0, which means the normal will have a higher influence
double weight = normal_distance_weight_ * (1.0 - nt.curvature);
double distance = std::abs (weight * d_normal + (1.0 - weight) * d_euclid);
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 (distance);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT, typename PointNT> std::size_t
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold) const
{
if (!normals_)
{
PCL_ERROR ("[pcl::SampleConsensusModelNormalPlane::countWithinDistance] No input dataset containing normals was given!\n");
return (0);
}
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
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, typename PointNT> std::size_t
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::countWithinDistanceStandard (
const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
{
std::size_t nr_p = 0;
// Obtain the plane normal
Eigen::Vector4f coeff = model_coefficients;
coeff[3] = 0.0f;
// Iterate through the 3d points and calculate the distances from them to the plane
for (; i < indices_->size (); ++i)
{
const PointT &pt = (*input_)[(*indices_)[i]];
const PointNT &nt = (*normals_)[(*indices_)[i]];
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
const Eigen::Vector4f p (pt.x, pt.y, pt.z, 0.0f);
const Eigen::Vector4f n (nt.normal_x, nt.normal_y, nt.normal_z, 0.0f);
const double d_euclid = std::abs (coeff.dot (p) + model_coefficients[3]);
// Calculate the angular distance between the point normal and the plane normal
double d_normal = std::abs (getAngle3D (n, coeff));
d_normal = (std::min) (d_normal, M_PI - d_normal);
// Weight with the point curvature. On flat surfaces, curvature -> 0, which means the normal will have a higher influence
const double weight = normal_distance_weight_ * (1.0 - nt.curvature);
if (std::abs (weight * d_normal + (1.0 - weight) * d_euclid) < threshold)
{
nr_p++;
}
}
return (nr_p);
}
//////////////////////////////////////////////////////////////////////////
#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
template <typename PointT, typename PointNT> std::size_t
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::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 normal_distance_weight_vec = _mm_set1_ps (normal_distance_weight_);
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 d_euclid_vec = pcl::SampleConsensusModelPlane<PointT>::dist4 (i, a_vec, b_vec, c_vec, d_vec, abs_help);
const __m128 d_normal_vec = getAcuteAngle3DSSE (
_mm_set_ps ((*normals_)[(*indices_)[i ]].normal_x,
(*normals_)[(*indices_)[i+1]].normal_x,
(*normals_)[(*indices_)[i+2]].normal_x,
(*normals_)[(*indices_)[i+3]].normal_x),
_mm_set_ps ((*normals_)[(*indices_)[i ]].normal_y,
(*normals_)[(*indices_)[i+1]].normal_y,
(*normals_)[(*indices_)[i+2]].normal_y,
(*normals_)[(*indices_)[i+3]].normal_y),
_mm_set_ps ((*normals_)[(*indices_)[i ]].normal_z,
(*normals_)[(*indices_)[i+1]].normal_z,
(*normals_)[(*indices_)[i+2]].normal_z,
(*normals_)[(*indices_)[i+3]].normal_z),
a_vec, b_vec, c_vec);
const __m128 weight_vec = _mm_mul_ps (normal_distance_weight_vec, _mm_sub_ps (_mm_set1_ps (1.0f),
_mm_set_ps ((*normals_)[(*indices_)[i ]].curvature,
(*normals_)[(*indices_)[i+1]].curvature,
(*normals_)[(*indices_)[i+2]].curvature,
(*normals_)[(*indices_)[i+3]].curvature)));
const __m128 dist = _mm_andnot_ps (abs_help, _mm_add_ps (_mm_mul_ps (weight_vec, d_normal_vec), _mm_mul_ps (_mm_sub_ps (_mm_set1_ps (1.0f), weight_vec), d_euclid_vec)));
const __m128 mask = _mm_cmplt_ps (dist, 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
}
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, typename PointNT> std::size_t
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::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 normal_distance_weight_vec = _mm256_set1_ps (normal_distance_weight_);
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 d_euclid_vec = pcl::SampleConsensusModelPlane<PointT>::dist8 (i, a_vec, b_vec, c_vec, d_vec, abs_help);
const __m256 d_normal_vec = getAcuteAngle3DAVX (
_mm256_set_ps ((*normals_)[(*indices_)[i ]].normal_x,
(*normals_)[(*indices_)[i+1]].normal_x,
(*normals_)[(*indices_)[i+2]].normal_x,
(*normals_)[(*indices_)[i+3]].normal_x,
(*normals_)[(*indices_)[i+4]].normal_x,
(*normals_)[(*indices_)[i+5]].normal_x,
(*normals_)[(*indices_)[i+6]].normal_x,
(*normals_)[(*indices_)[i+7]].normal_x),
_mm256_set_ps ((*normals_)[(*indices_)[i ]].normal_y,
(*normals_)[(*indices_)[i+1]].normal_y,
(*normals_)[(*indices_)[i+2]].normal_y,
(*normals_)[(*indices_)[i+3]].normal_y,
(*normals_)[(*indices_)[i+4]].normal_y,
(*normals_)[(*indices_)[i+5]].normal_y,
(*normals_)[(*indices_)[i+6]].normal_y,
(*normals_)[(*indices_)[i+7]].normal_y),
_mm256_set_ps ((*normals_)[(*indices_)[i ]].normal_z,
(*normals_)[(*indices_)[i+1]].normal_z,
(*normals_)[(*indices_)[i+2]].normal_z,
(*normals_)[(*indices_)[i+3]].normal_z,
(*normals_)[(*indices_)[i+4]].normal_z,
(*normals_)[(*indices_)[i+5]].normal_z,
(*normals_)[(*indices_)[i+6]].normal_z,
(*normals_)[(*indices_)[i+7]].normal_z),
a_vec, b_vec, c_vec);
const __m256 weight_vec = _mm256_mul_ps (normal_distance_weight_vec, _mm256_sub_ps (_mm256_set1_ps (1.0f),
_mm256_set_ps ((*normals_)[(*indices_)[i ]].curvature,
(*normals_)[(*indices_)[i+1]].curvature,
(*normals_)[(*indices_)[i+2]].curvature,
(*normals_)[(*indices_)[i+3]].curvature,
(*normals_)[(*indices_)[i+4]].curvature,
(*normals_)[(*indices_)[i+5]].curvature,
(*normals_)[(*indices_)[i+6]].curvature,
(*normals_)[(*indices_)[i+7]].curvature)));
const __m256 dist = _mm256_andnot_ps (abs_help, _mm256_add_ps (_mm256_mul_ps (weight_vec, d_normal_vec), _mm256_mul_ps (_mm256_sub_ps (_mm256_set1_ps (1.0f), weight_vec), d_euclid_vec)));
const __m256 mask = _mm256_cmp_ps (dist, 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
}
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, typename PointNT> void
pcl::SampleConsensusModelNormalPlane<PointT, PointNT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
if (!normals_)
{
PCL_ERROR ("[pcl::SampleConsensusModelNormalPlane::getDistancesToModel] No input dataset containing normals was given!\n");
return;
}
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
{
distances.clear ();
return;
}
// Obtain the plane normal
Eigen::Vector4f coeff = model_coefficients;
coeff[3] = 0.0f;
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)
{
const PointT &pt = (*input_)[(*indices_)[i]];
const PointNT &nt = (*normals_)[(*indices_)[i]];
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f p (pt.x, pt.y, pt.z, 0.0f);
Eigen::Vector4f n (nt.normal_x, nt.normal_y, nt.normal_z, 0.0f);
double d_euclid = std::abs (coeff.dot (p) + model_coefficients[3]);
// Calculate the angular distance between the point normal and the plane normal
double d_normal = std::abs (getAngle3D (n, coeff));
d_normal = (std::min) (d_normal, M_PI - d_normal);
// Weight with the point curvature. On flat surfaces, curvature -> 0, which means the normal will have a higher influence
double weight = normal_distance_weight_ * (1.0 - nt.curvature);
distances[i] = std::abs (weight * d_normal + (1.0 - weight) * d_euclid);
}
}
#define PCL_INSTANTIATE_SampleConsensusModelNormalPlane(PointT, PointNT) template class PCL_EXPORTS pcl::SampleConsensusModelNormalPlane<PointT, PointNT>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_PLANE_H_
pcl::SampleConsensusModelNormalPlane
SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface no...
Definition: sac_model_normal_plane.h:77
pcl::SampleConsensusModelNormalPlane::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_normal_plane.hpp:106
pcl::SampleConsensusModelNormalPlane::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_normal_plane.hpp:50
pcl::SampleConsensusModelNormalPlane::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_normal_plane.hpp:293
pcl::SampleConsensusModelNormalPlane::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_normal_plane.hpp:130
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition: sac_model_plane.h:143
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl::getAngle3D
double getAngle3D(const Eigen::Vector4f &v1, const Eigen::Vector4f &v2, const bool in_degree=false)
Compute the smallest angle between two 3D vectors in radians (default) or degree.
Definition: common.hpp:47
pcl::geometry::distance
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
M_PI
#define M_PI
Definition: pcl_macros.h:201
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:675