478 lines
16 KiB
C++
478 lines
16 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*
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*/
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#pragma once
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#include <pcl/common/transforms.h>
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#if defined(__SSE2__)
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#include <xmmintrin.h>
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#endif
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#if defined(__AVX__)
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#include <immintrin.h>
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#endif
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#include <algorithm>
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#include <cmath>
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#include <cstddef>
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#include <vector>
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namespace pcl
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{
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namespace detail
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{
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/** A helper struct to apply an SO3 or SE3 transform to a 3D point.
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* Supports single and double precision transform matrices. */
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template<typename Scalar>
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struct Transformer
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{
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const Eigen::Matrix<Scalar, 4, 4>& tf;
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/** Construct a transformer object.
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* The transform matrix is captured by const reference. Make sure that it does not go out of scope before this
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* object does. */
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Transformer (const Eigen::Matrix<Scalar, 4, 4>& transform) : tf (transform) { };
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/** Apply SO3 transform (top-left corner of the transform matrix).
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* \param[in] src input 3D point (pointer to 3 floats)
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* \param[out] tgt output 3D point (pointer to 4 floats), can be the same as input. The fourth element is set to 0. */
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void so3 (const float* src, float* tgt) const
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{
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const Scalar p[3] = { src[0], src[1], src[2] }; // need this when src == tgt
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tgt[0] = static_cast<float> (tf (0, 0) * p[0] + tf (0, 1) * p[1] + tf (0, 2) * p[2]);
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tgt[1] = static_cast<float> (tf (1, 0) * p[0] + tf (1, 1) * p[1] + tf (1, 2) * p[2]);
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tgt[2] = static_cast<float> (tf (2, 0) * p[0] + tf (2, 1) * p[1] + tf (2, 2) * p[2]);
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tgt[3] = 0;
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}
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/** Apply SE3 transform.
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* \param[in] src input 3D point (pointer to 3 floats)
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* \param[out] tgt output 3D point (pointer to 4 floats), can be the same as input. The fourth element is set to 1. */
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void se3 (const float* src, float* tgt) const
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{
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const Scalar p[3] = { src[0], src[1], src[2] }; // need this when src == tgt
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tgt[0] = static_cast<float> (tf (0, 0) * p[0] + tf (0, 1) * p[1] + tf (0, 2) * p[2] + tf (0, 3));
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tgt[1] = static_cast<float> (tf (1, 0) * p[0] + tf (1, 1) * p[1] + tf (1, 2) * p[2] + tf (1, 3));
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tgt[2] = static_cast<float> (tf (2, 0) * p[0] + tf (2, 1) * p[1] + tf (2, 2) * p[2] + tf (2, 3));
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tgt[3] = 1;
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}
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};
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#if defined(__SSE2__)
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/** Optimized version for single-precision transforms using SSE2 intrinsics. */
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template<>
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struct Transformer<float>
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{
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/// Columns of the transform matrix stored in XMM registers.
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__m128 c[4];
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Transformer(const Eigen::Matrix4f& tf)
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{
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for (std::size_t i = 0; i < 4; ++i)
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c[i] = _mm_load_ps (tf.col (i).data ());
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}
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void so3 (const float* src, float* tgt) const
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{
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__m128 p0 = _mm_mul_ps (_mm_load_ps1 (&src[0]), c[0]);
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__m128 p1 = _mm_mul_ps (_mm_load_ps1 (&src[1]), c[1]);
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__m128 p2 = _mm_mul_ps (_mm_load_ps1 (&src[2]), c[2]);
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_mm_store_ps (tgt, _mm_add_ps(p0, _mm_add_ps(p1, p2)));
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}
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void se3 (const float* src, float* tgt) const
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{
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__m128 p0 = _mm_mul_ps (_mm_load_ps1 (&src[0]), c[0]);
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__m128 p1 = _mm_mul_ps (_mm_load_ps1 (&src[1]), c[1]);
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__m128 p2 = _mm_mul_ps (_mm_load_ps1 (&src[2]), c[2]);
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_mm_store_ps (tgt, _mm_add_ps(p0, _mm_add_ps(p1, _mm_add_ps(p2, c[3]))));
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}
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};
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#if !defined(__AVX__)
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/** Optimized version for double-precision transform using SSE2 intrinsics. */
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template<>
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struct Transformer<double>
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{
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/// Columns of the transform matrix stored in XMM registers.
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__m128d c[4][2];
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Transformer(const Eigen::Matrix4d& tf)
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{
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for (std::size_t i = 0; i < 4; ++i)
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{
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c[i][0] = _mm_load_pd (tf.col (i).data () + 0);
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c[i][1] = _mm_load_pd (tf.col (i).data () + 2);
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}
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}
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void so3 (const float* src, float* tgt) const
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{
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__m128d xx = _mm_cvtps_pd (_mm_load_ps1 (&src[0]));
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__m128d p0 = _mm_mul_pd (xx, c[0][0]);
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__m128d p1 = _mm_mul_pd (xx, c[0][1]);
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for (std::size_t i = 1; i < 3; ++i)
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{
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__m128d vv = _mm_cvtps_pd (_mm_load_ps1 (&src[i]));
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p0 = _mm_add_pd (_mm_mul_pd (vv, c[i][0]), p0);
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p1 = _mm_add_pd (_mm_mul_pd (vv, c[i][1]), p1);
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}
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_mm_store_ps (tgt, _mm_movelh_ps (_mm_cvtpd_ps (p0), _mm_cvtpd_ps (p1)));
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}
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void se3 (const float* src, float* tgt) const
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{
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__m128d p0 = c[3][0];
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__m128d p1 = c[3][1];
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for (std::size_t i = 0; i < 3; ++i)
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{
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__m128d vv = _mm_cvtps_pd (_mm_load_ps1 (&src[i]));
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p0 = _mm_add_pd (_mm_mul_pd (vv, c[i][0]), p0);
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p1 = _mm_add_pd (_mm_mul_pd (vv, c[i][1]), p1);
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}
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_mm_store_ps (tgt, _mm_movelh_ps (_mm_cvtpd_ps (p0), _mm_cvtpd_ps (p1)));
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}
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};
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#else
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/** Optimized version for double-precision transform using AVX intrinsics. */
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template<>
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struct Transformer<double>
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{
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__m256d c[4];
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Transformer(const Eigen::Matrix4d& tf)
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{
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for (std::size_t i = 0; i < 4; ++i)
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c[i] = _mm256_load_pd (tf.col (i).data ());
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}
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void so3 (const float* src, float* tgt) const
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{
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__m256d p0 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[0])), c[0]);
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__m256d p1 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[1])), c[1]);
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__m256d p2 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[2])), c[2]);
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_mm_store_ps (tgt, _mm256_cvtpd_ps (_mm256_add_pd(p0, _mm256_add_pd(p1, p2))));
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}
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void se3 (const float* src, float* tgt) const
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{
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__m256d p0 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[0])), c[0]);
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__m256d p1 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[1])), c[1]);
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__m256d p2 = _mm256_mul_pd (_mm256_cvtps_pd (_mm_load_ps1 (&src[2])), c[2]);
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_mm_store_ps (tgt, _mm256_cvtpd_ps (_mm256_add_pd(p0, _mm256_add_pd(p1, _mm256_add_pd(p2, c[3])))));
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}
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};
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#endif // !defined(__AVX__)
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#endif // defined(__SSE2__)
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} // namespace detail
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template <typename PointT, typename Scalar> void
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transformPointCloud (const pcl::PointCloud<PointT> &cloud_in,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 4, 4> &transform,
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bool copy_all_fields)
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{
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if (&cloud_in != &cloud_out)
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{
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cloud_out.header = cloud_in.header;
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cloud_out.is_dense = cloud_in.is_dense;
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cloud_out.reserve (cloud_in.size ());
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if (copy_all_fields)
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cloud_out.assign (cloud_in.begin (), cloud_in.end (), cloud_in.width);
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else
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cloud_out.resize (cloud_in.width, cloud_in.height);
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cloud_out.sensor_orientation_ = cloud_in.sensor_orientation_;
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cloud_out.sensor_origin_ = cloud_in.sensor_origin_;
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}
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pcl::detail::Transformer<Scalar> tf (transform);
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if (cloud_in.is_dense)
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{
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// If the dataset is dense, simply transform it!
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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tf.se3 (cloud_in[i].data, cloud_out[i].data);
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}
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else
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{
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// Dataset might contain NaNs and Infs, so check for them first,
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// otherwise we get errors during the multiplication (?)
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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{
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if (!std::isfinite (cloud_in[i].x) ||
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!std::isfinite (cloud_in[i].y) ||
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!std::isfinite (cloud_in[i].z))
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continue;
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tf.se3 (cloud_in[i].data, cloud_out[i].data);
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}
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}
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}
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template <typename PointT, typename Scalar> void
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transformPointCloud (const pcl::PointCloud<PointT> &cloud_in,
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const Indices &indices,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 4, 4> &transform,
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bool copy_all_fields)
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{
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std::size_t npts = indices.size ();
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// In order to transform the data, we need to remove NaNs
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cloud_out.is_dense = cloud_in.is_dense;
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cloud_out.header = cloud_in.header;
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cloud_out.width = static_cast<int> (npts);
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cloud_out.height = 1;
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cloud_out.resize (npts);
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cloud_out.sensor_orientation_ = cloud_in.sensor_orientation_;
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cloud_out.sensor_origin_ = cloud_in.sensor_origin_;
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pcl::detail::Transformer<Scalar> tf (transform);
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if (cloud_in.is_dense)
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{
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// If the dataset is dense, simply transform it!
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for (std::size_t i = 0; i < npts; ++i)
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{
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// Copy fields first, then transform xyz data
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if (copy_all_fields)
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cloud_out[i] = cloud_in[indices[i]];
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tf.se3 (cloud_in[indices[i]].data, cloud_out[i].data);
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}
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}
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else
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{
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// Dataset might contain NaNs and Infs, so check for them first,
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// otherwise we get errors during the multiplication (?)
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for (std::size_t i = 0; i < npts; ++i)
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{
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if (copy_all_fields)
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cloud_out[i] = cloud_in[indices[i]];
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if (!std::isfinite (cloud_in[indices[i]].x) ||
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!std::isfinite (cloud_in[indices[i]].y) ||
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!std::isfinite (cloud_in[indices[i]].z))
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continue;
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tf.se3 (cloud_in[indices[i]].data, cloud_out[i].data);
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}
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}
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}
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template <typename PointT, typename Scalar> void
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transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 4, 4> &transform,
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bool copy_all_fields)
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{
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if (&cloud_in != &cloud_out)
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{
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// Note: could be replaced by cloud_out = cloud_in
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cloud_out.header = cloud_in.header;
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cloud_out.is_dense = cloud_in.is_dense;
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cloud_out.reserve (cloud_in.size ());
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if (copy_all_fields)
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cloud_out.assign (cloud_in.begin (), cloud_in.end (), cloud_in.width);
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else
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cloud_out.resize (cloud_in.width, cloud_in.height);
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cloud_out.sensor_orientation_ = cloud_in.sensor_orientation_;
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cloud_out.sensor_origin_ = cloud_in.sensor_origin_;
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}
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pcl::detail::Transformer<Scalar> tf (transform);
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// If the data is dense, we don't need to check for NaN
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if (cloud_in.is_dense)
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{
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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{
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tf.se3 (cloud_in[i].data, cloud_out[i].data);
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tf.so3 (cloud_in[i].data_n, cloud_out[i].data_n);
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}
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}
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// Dataset might contain NaNs and Infs, so check for them first.
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else
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{
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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{
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if (!std::isfinite (cloud_in[i].x) ||
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!std::isfinite (cloud_in[i].y) ||
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!std::isfinite (cloud_in[i].z))
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continue;
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tf.se3 (cloud_in[i].data, cloud_out[i].data);
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tf.so3 (cloud_in[i].data_n, cloud_out[i].data_n);
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}
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}
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}
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template <typename PointT, typename Scalar> void
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transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in,
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const Indices &indices,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 4, 4> &transform,
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bool copy_all_fields)
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{
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std::size_t npts = indices.size ();
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// In order to transform the data, we need to remove NaNs
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cloud_out.is_dense = cloud_in.is_dense;
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cloud_out.header = cloud_in.header;
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cloud_out.width = static_cast<int> (npts);
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cloud_out.height = 1;
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cloud_out.resize (npts);
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cloud_out.sensor_orientation_ = cloud_in.sensor_orientation_;
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cloud_out.sensor_origin_ = cloud_in.sensor_origin_;
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pcl::detail::Transformer<Scalar> tf (transform);
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// If the data is dense, we don't need to check for NaN
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if (cloud_in.is_dense)
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{
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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{
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// Copy fields first, then transform
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if (copy_all_fields)
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cloud_out[i] = cloud_in[indices[i]];
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tf.se3 (cloud_in[indices[i]].data, cloud_out[i].data);
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tf.so3 (cloud_in[indices[i]].data_n, cloud_out[i].data_n);
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}
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}
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// Dataset might contain NaNs and Infs, so check for them first.
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else
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{
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for (std::size_t i = 0; i < cloud_out.size (); ++i)
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{
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// Copy fields first, then transform
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if (copy_all_fields)
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cloud_out[i] = cloud_in[indices[i]];
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if (!std::isfinite (cloud_in[indices[i]].x) ||
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!std::isfinite (cloud_in[indices[i]].y) ||
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!std::isfinite (cloud_in[indices[i]].z))
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continue;
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tf.se3 (cloud_in[indices[i]].data, cloud_out[i].data);
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tf.so3 (cloud_in[indices[i]].data_n, cloud_out[i].data_n);
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}
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}
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}
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template <typename PointT, typename Scalar> inline void
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transformPointCloud (const pcl::PointCloud<PointT> &cloud_in,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 3, 1> &offset,
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const Eigen::Quaternion<Scalar> &rotation,
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bool copy_all_fields)
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{
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Eigen::Translation<Scalar, 3> translation (offset);
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// Assemble an Eigen Transform
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Eigen::Transform<Scalar, 3, Eigen::Affine> t (translation * rotation);
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transformPointCloud (cloud_in, cloud_out, t, copy_all_fields);
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}
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template <typename PointT, typename Scalar> inline void
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transformPointCloudWithNormals (const pcl::PointCloud<PointT> &cloud_in,
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pcl::PointCloud<PointT> &cloud_out,
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const Eigen::Matrix<Scalar, 3, 1> &offset,
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const Eigen::Quaternion<Scalar> &rotation,
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bool copy_all_fields)
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{
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Eigen::Translation<Scalar, 3> translation (offset);
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// Assemble an Eigen Transform
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Eigen::Transform<Scalar, 3, Eigen::Affine> t (translation * rotation);
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transformPointCloudWithNormals (cloud_in, cloud_out, t, copy_all_fields);
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}
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template <typename PointT, typename Scalar> inline PointT
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transformPoint (const PointT &point, const Eigen::Transform<Scalar, 3, Eigen::Affine> &transform)
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{
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PointT ret = point;
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pcl::detail::Transformer<Scalar> tf (transform.matrix ());
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tf.se3 (point.data, ret.data);
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return (ret);
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}
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template <typename PointT, typename Scalar> inline PointT
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transformPointWithNormal (const PointT &point, const Eigen::Transform<Scalar, 3, Eigen::Affine> &transform)
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{
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PointT ret = point;
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pcl::detail::Transformer<Scalar> tf (transform.matrix ());
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tf.se3 (point.data, ret.data);
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tf.so3 (point.data_n, ret.data_n);
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return (ret);
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}
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template <typename PointT, typename Scalar> double
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getPrincipalTransformation (const pcl::PointCloud<PointT> &cloud,
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Eigen::Transform<Scalar, 3, Eigen::Affine> &transform)
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{
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EIGEN_ALIGN16 Eigen::Matrix<Scalar, 3, 3> covariance_matrix;
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Eigen::Matrix<Scalar, 4, 1> centroid;
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pcl::computeMeanAndCovarianceMatrix (cloud, covariance_matrix, centroid);
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EIGEN_ALIGN16 Eigen::Matrix<Scalar, 3, 3> eigen_vects;
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Eigen::Matrix<Scalar, 3, 1> eigen_vals;
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pcl::eigen33 (covariance_matrix, eigen_vects, eigen_vals);
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double rel1 = eigen_vals.coeff (0) / eigen_vals.coeff (1);
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double rel2 = eigen_vals.coeff (1) / eigen_vals.coeff (2);
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transform.translation () = centroid.head (3);
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transform.linear () = eigen_vects;
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return (std::min (rel1, rel2));
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}
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} // namespace pcl
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