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eigen.hpp
/*
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* Author: Anatoly Baskeheev, Itseez Ltd, (myname.mysurname@mycompany.com)
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
// The computeRoots function included in this is based on materials
// covered by the following copyright and license:
//
// Geometric Tools, LLC
// Copyright (c) 1998-2010
// Distributed under the Boost Software License, Version 1.0.
//
// Permission is hereby granted, free of charge, to any person or organization
// obtaining a copy of the software and accompanying documentation covered by
// this license (the "Software") to use, reproduce, display, distribute,
// execute, and transmit the Software, and to prepare derivative works of the
// Software, and to permit third-parties to whom the Software is furnished to
// do so, all subject to the following:
//
// The copyright notices in the Software and this entire statement, including
// the above license grant, this restriction and the following disclaimer,
// must be included in all copies of the Software, in whole or in part, and
// all derivative works of the Software, unless such copies or derivative
// works are solely in the form of machine-executable object code generated by
// a source language processor.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
// SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
// FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
// ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
// DEALINGS IN THE SOFTWARE.
#ifndef PCL_GPU_FEATURES_EIGEN_HPP_
#define PCL_GPU_FEATURES_EIGEN_HPP_
#include <limits>
#include <pcl/gpu/utils/device/algorithm.hpp>
#include <pcl/gpu/utils/device/vector_math.hpp>
namespace pcl
{
namespace device
{
__device__ __forceinline__ void computeRoots2(const float& b, const float& c, float3& roots)
{
roots.x = 0.f;
float d = b * b - 4.f * c;
if (d < 0.f) // no real roots!!!! THIS SHOULD NOT HAPPEN!
d = 0.f;
float sd = sqrtf(d);
roots.z = 0.5f * (b + sd);
roots.y = 0.5f * (b - sd);
}
__device__ __forceinline__ void computeRoots3(float c0, float c1, float c2, float3& roots)
{
if ( std::abs(c0) < std::numeric_limits<float>::epsilon())// one root is 0 -> quadratic equation
{
computeRoots2 (c2, c1, roots);
}
else
{
const float s_inv3 = 1.f/3.f;
const float s_sqrt3 = sqrtf(3.f);
// Construct the parameters used in classifying the roots of the equation
// and in solving the equation for the roots in closed form.
float c2_over_3 = c2 * s_inv3;
float a_over_3 = (c1 - c2*c2_over_3)*s_inv3;
if (a_over_3 > 0.f)
a_over_3 = 0.f;
float half_b = 0.5f * (c0 + c2_over_3 * (2.f * c2_over_3 * c2_over_3 - c1));
float q = half_b * half_b + a_over_3 * a_over_3 * a_over_3;
if (q > 0.f)
q = 0.f;
// Compute the eigenvalues by solving for the roots of the polynomial.
float rho = sqrtf(-a_over_3);
float theta = std::atan2 (sqrtf (-q), half_b)*s_inv3;
float cos_theta = __cosf (theta);
float sin_theta = __sinf (theta);
roots.x = c2_over_3 + 2.f * rho * cos_theta;
roots.y = c2_over_3 - rho * (cos_theta + s_sqrt3 * sin_theta);
roots.z = c2_over_3 - rho * (cos_theta - s_sqrt3 * sin_theta);
// Sort in increasing order.
if (roots.x >= roots.y)
swap(roots.x, roots.y);
if (roots.y >= roots.z)
{
swap(roots.y, roots.z);
if (roots.x >= roots.y)
swap (roots.x, roots.y);
}
if (roots.x <= 0) // eigenval for symmetric positive semi-definite matrix can not be negative! Set it to 0
computeRoots2 (c2, c1, roots);
}
}
struct Eigen33
{
public:
template<int Rows>
struct MiniMat
{
float3 data[Rows];
__device__ __host__ __forceinline__ float3& operator[](int i) { return data[i]; }
__device__ __host__ __forceinline__ const float3& operator[](int i) const { return data[i]; }
};
using Mat33 = MiniMat<3>;
using Mat43 = MiniMat<4>;
static __forceinline__ __device__ float3 unitOrthogonal (const float3& src)
{
float3 perp;
/* Let us compute the crossed product of *this with a vector
* that is not too close to being colinear to *this.
*/
/* unless the x and y coords are both close to zero, we can
* simply take ( -y, x, 0 ) and normalize it.
*/
if(!isMuchSmallerThan(src.x, src.z) || !isMuchSmallerThan(src.y, src.z))
{
float invnm = rsqrtf(src.x*src.x + src.y*src.y);
perp.x = -src.y * invnm;
perp.y = src.x * invnm;
perp.z = 0.0f;
}
/* if both x and y are close to zero, then the vector is close
* to the z-axis, so it's far from colinear to the x-axis for instance.
* So we take the crossed product with (1,0,0) and normalize it.
*/
else
{
float invnm = rsqrtf(src.z * src.z + src.y * src.y);
perp.x = 0.0f;
perp.y = -src.z * invnm;
perp.z = src.y * invnm;
}
return perp;
}
__device__ __forceinline__ Eigen33(volatile float* mat_pkg_arg) : mat_pkg(mat_pkg_arg) {}
__device__ __forceinline__ void compute(Mat33& tmp, Mat33& vec_tmp, Mat33& evecs, float3& evals)
{
// Scale the matrix so its entries are in [-1,1]. The scaling is applied
// only when at least one matrix entry has magnitude larger than 1.
float max01 = fmaxf( std::abs(mat_pkg[0]), std::abs(mat_pkg[1]) );
float max23 = fmaxf( std::abs(mat_pkg[2]), std::abs(mat_pkg[3]) );
float max45 = fmaxf( std::abs(mat_pkg[4]), std::abs(mat_pkg[5]) );
float m0123 = fmaxf( max01, max23);
float scale = fmaxf( max45, m0123);
if (scale <= std::numeric_limits<float>::min())
scale = 1.f;
mat_pkg[0] /= scale;
mat_pkg[1] /= scale;
mat_pkg[2] /= scale;
mat_pkg[3] /= scale;
mat_pkg[4] /= scale;
mat_pkg[5] /= scale;
// The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0. The
// eigenvalues are the roots to this equation, all guaranteed to be
// real-valued, because the matrix is symmetric.
float c0 = m00() * m11() * m22()
+ 2.f * m01() * m02() * m12()
- m00() * m12() * m12()
- m11() * m02() * m02()
- m22() * m01() * m01();
float c1 = m00() * m11() -
m01() * m01() +
m00() * m22() -
m02() * m02() +
m11() * m22() -
m12() * m12();
float c2 = m00() + m11() + m22();
computeRoots3(c0, c1, c2, evals);
if(evals.z - evals.x <= std::numeric_limits<float>::epsilon())
{
evecs[0] = make_float3(1.f, 0.f, 0.f);
evecs[1] = make_float3(0.f, 1.f, 0.f);
evecs[2] = make_float3(0.f, 0.f, 1.f);
}
else if (evals.y - evals.x <= std::numeric_limits<float>::epsilon() )
{
// first and second equal
tmp[0] = row0(); tmp[1] = row1(); tmp[2] = row2();
tmp[0].x -= evals.z; tmp[1].y -= evals.z; tmp[2].z -= evals.z;
vec_tmp[0] = cross(tmp[0], tmp[1]);
vec_tmp[1] = cross(tmp[0], tmp[2]);
vec_tmp[2] = cross(tmp[1], tmp[2]);
float len1 = dot (vec_tmp[0], vec_tmp[0]);
float len2 = dot (vec_tmp[1], vec_tmp[1]);
float len3 = dot (vec_tmp[2], vec_tmp[2]);
if (len1 >= len2 && len1 >= len3)
{
evecs[2] = vec_tmp[0] * rsqrtf (len1);
}
else if (len2 >= len1 && len2 >= len3)
{
evecs[2] = vec_tmp[1] * rsqrtf (len2);
}
else
{
evecs[2] = vec_tmp[2] * rsqrtf (len3);
}
evecs[1] = unitOrthogonal(evecs[2]);
evecs[0] = cross(evecs[1], evecs[2]);
}
else if (evals.z - evals.y <= std::numeric_limits<float>::epsilon() )
{
// second and third equal
tmp[0] = row0(); tmp[1] = row1(); tmp[2] = row2();
tmp[0].x -= evals.x; tmp[1].y -= evals.x; tmp[2].z -= evals.x;
vec_tmp[0] = cross(tmp[0], tmp[1]);
vec_tmp[1] = cross(tmp[0], tmp[2]);
vec_tmp[2] = cross(tmp[1], tmp[2]);
float len1 = dot(vec_tmp[0], vec_tmp[0]);
float len2 = dot(vec_tmp[1], vec_tmp[1]);
float len3 = dot(vec_tmp[2], vec_tmp[2]);
if (len1 >= len2 && len1 >= len3)
{
evecs[0] = vec_tmp[0] * rsqrtf(len1);
}
else if (len2 >= len1 && len2 >= len3)
{
evecs[0] = vec_tmp[1] * rsqrtf(len2);
}
else
{
evecs[0] = vec_tmp[2] * rsqrtf(len3);
}
evecs[1] = unitOrthogonal( evecs[0] );
evecs[2] = cross(evecs[0], evecs[1]);
}
else
{
tmp[0] = row0(); tmp[1] = row1(); tmp[2] = row2();
tmp[0].x -= evals.z; tmp[1].y -= evals.z; tmp[2].z -= evals.z;
vec_tmp[0] = cross(tmp[0], tmp[1]);
vec_tmp[1] = cross(tmp[0], tmp[2]);
vec_tmp[2] = cross(tmp[1], tmp[2]);
float len1 = dot(vec_tmp[0], vec_tmp[0]);
float len2 = dot(vec_tmp[1], vec_tmp[1]);
float len3 = dot(vec_tmp[2], vec_tmp[2]);
float mmax[3];
unsigned int min_el = 2;
unsigned int max_el = 2;
if (len1 >= len2 && len1 >= len3)
{
mmax[2] = len1;
evecs[2] = vec_tmp[0] * rsqrtf (len1);
}
else if (len2 >= len1 && len2 >= len3)
{
mmax[2] = len2;
evecs[2] = vec_tmp[1] * rsqrtf (len2);
}
else
{
mmax[2] = len3;
evecs[2] = vec_tmp[2] * rsqrtf (len3);
}
tmp[0] = row0(); tmp[1] = row1(); tmp[2] = row2();
tmp[0].x -= evals.y; tmp[1].y -= evals.y; tmp[2].z -= evals.y;
vec_tmp[0] = cross(tmp[0], tmp[1]);
vec_tmp[1] = cross(tmp[0], tmp[2]);
vec_tmp[2] = cross(tmp[1], tmp[2]);
len1 = dot(vec_tmp[0], vec_tmp[0]);
len2 = dot(vec_tmp[1], vec_tmp[1]);
len3 = dot(vec_tmp[2], vec_tmp[2]);
if (len1 >= len2 && len1 >= len3)
{
mmax[1] = len1;
evecs[1] = vec_tmp[0] * rsqrtf (len1);
min_el = len1 <= mmax[min_el] ? 1 : min_el;
max_el = len1 > mmax[max_el] ? 1 : max_el;
}
else if (len2 >= len1 && len2 >= len3)
{
mmax[1] = len2;
evecs[1] = vec_tmp[1] * rsqrtf (len2);
min_el = len2 <= mmax[min_el] ? 1 : min_el;
max_el = len2 > mmax[max_el] ? 1 : max_el;
}
else
{
mmax[1] = len3;
evecs[1] = vec_tmp[2] * rsqrtf (len3);
min_el = len3 <= mmax[min_el] ? 1 : min_el;
max_el = len3 > mmax[max_el] ? 1 : max_el;
}
tmp[0] = row0(); tmp[1] = row1(); tmp[2] = row2();
tmp[0].x -= evals.x; tmp[1].y -= evals.x; tmp[2].z -= evals.x;
vec_tmp[0] = cross(tmp[0], tmp[1]);
vec_tmp[1] = cross(tmp[0], tmp[2]);
vec_tmp[2] = cross(tmp[1], tmp[2]);
len1 = dot (vec_tmp[0], vec_tmp[0]);
len2 = dot (vec_tmp[1], vec_tmp[1]);
len3 = dot (vec_tmp[2], vec_tmp[2]);
if (len1 >= len2 && len1 >= len3)
{
mmax[0] = len1;
evecs[0] = vec_tmp[0] * rsqrtf (len1);
min_el = len3 <= mmax[min_el] ? 0 : min_el;
max_el = len3 > mmax[max_el] ? 0 : max_el;
}
else if (len2 >= len1 && len2 >= len3)
{
mmax[0] = len2;
evecs[0] = vec_tmp[1] * rsqrtf (len2);
min_el = len3 <= mmax[min_el] ? 0 : min_el;
max_el = len3 > mmax[max_el] ? 0 : max_el;
}
else
{
mmax[0] = len3;
evecs[0] = vec_tmp[2] * rsqrtf (len3);
min_el = len3 <= mmax[min_el] ? 0 : min_el;
max_el = len3 > mmax[max_el] ? 0 : max_el;
}
unsigned mid_el = 3 - min_el - max_el;
evecs[min_el] = normalized( cross( evecs[(min_el+1) % 3], evecs[(min_el+2) % 3] ) );
evecs[mid_el] = normalized( cross( evecs[(mid_el+1) % 3], evecs[(mid_el+2) % 3] ) );
}
// Rescale back to the original size.
evals *= scale;
}
private:
volatile float* mat_pkg;
__device__ __forceinline__ float m00() const { return mat_pkg[0]; }
__device__ __forceinline__ float m01() const { return mat_pkg[1]; }
__device__ __forceinline__ float m02() const { return mat_pkg[2]; }
__device__ __forceinline__ float m10() const { return mat_pkg[1]; }
__device__ __forceinline__ float m11() const { return mat_pkg[3]; }
__device__ __forceinline__ float m12() const { return mat_pkg[4]; }
__device__ __forceinline__ float m20() const { return mat_pkg[2]; }
__device__ __forceinline__ float m21() const { return mat_pkg[4]; }
__device__ __forceinline__ float m22() const { return mat_pkg[5]; }
__device__ __forceinline__ float3 row0() const { return make_float3( m00(), m01(), m02() ); }
__device__ __forceinline__ float3 row1() const { return make_float3( m10(), m11(), m12() ); }
__device__ __forceinline__ float3 row2() const { return make_float3( m20(), m21(), m22() ); }
__device__ __forceinline__ static bool isMuchSmallerThan (float x, float y)
{
// copied from <eigen>/include/Eigen/src/Core/NumTraits.h
constexpr float prec_sqr = std::numeric_limits<float>::epsilon() * std::numeric_limits<float>::epsilon();
return x * x <= prec_sqr * y * y;
}
};
}
}
#endif /* PCL_GPU_FEATURES_EIGEN_HPP_ */
pcl::device::normalized
__device__ __forceinline__ float3 normalized(const float3 &v)
Definition utils.hpp:101
pcl::device::dot
__device__ __forceinline__ float dot(const float3 &v1, const float3 &v2)
Definition utils.hpp:59
pcl::device::computeRoots3
__device__ __forceinline__ void computeRoots3(float c0, float c1, float c2, float3 &roots)
Definition eigen.hpp:115
pcl::device::computeRoots2
__device__ __forceinline__ void computeRoots2(const float &b, const float &c, float3 &roots)
Definition eigen.hpp:102
pcl::device::swap
__device__ __host__ __forceinline__ void swap(T &a, T &b)
Definition utils.hpp:53
pcl::device::cross
__device__ __host__ __forceinline__ float3 cross(const float3 &v1, const float3 &v2)
Definition utils.hpp:107
pcl
Definition convolution.h:46
pcl::device::Eigen33::MiniMat
Definition eigen.hpp:168
pcl::device::Eigen33::MiniMat::data
float3 data[Rows]
Definition eigen.hpp:169
pcl::device::Eigen33::MiniMat::operator[]
__device__ __host__ __forceinline__ float3 & operator[](int i)
Definition eigen.hpp:170
pcl::device::Eigen33::MiniMat::operator[]
__device__ __host__ __forceinline__ const float3 & operator[](int i) const
Definition eigen.hpp:171
pcl::device::Eigen33
Definition eigen.hpp:164
pcl::device::Eigen33::compute
__device__ __forceinline__ void compute(Mat33 &tmp, Mat33 &vec_tmp, Mat33 &evecs, float3 &evals)
Definition eigen.hpp:210
pcl::device::Eigen33::Mat33
MiniMat< 3 > Mat33
Definition eigen.hpp:173
pcl::device::Eigen33::unitOrthogonal
static __forceinline__ __device__ float3 unitOrthogonal(const float3 &src)
Definition eigen.hpp:177
pcl::device::Eigen33::Eigen33
__device__ __forceinline__ Eigen33(volatile float *mat_pkg_arg)
Definition eigen.hpp:209
pcl::device::Eigen33::Mat43
MiniMat< 4 > Mat43
Definition eigen.hpp:174