239 lines
8.8 KiB
C++
239 lines
8.8 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) 2009, 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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* $Id$
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*
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*/
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
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#define PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
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#if defined __GNUC__
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# pragma GCC system_header
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#endif
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#include <boost/math/distributions/binomial.hpp>
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#include <pcl/sample_consensus/prosac.h>
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//////////////////////////////////////////////////////////////////////////
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// Variable naming uses capital letters to make the comparison with the original paper easier
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template<typename PointT> bool
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pcl::ProgressiveSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
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{
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// Warn and exit if no threshold was set
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if (threshold_ == DBL_MAX)
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{
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PCL_ERROR ("[pcl::ProgressiveSampleConsensus::computeModel] No threshold set!\n");
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return (false);
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}
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// Initialize some PROSAC constants
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const int T_N = 200000;
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const std::size_t N = sac_model_->indices_->size ();
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const std::size_t m = sac_model_->getSampleSize ();
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float T_n = static_cast<float> (T_N);
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for (unsigned int i = 0; i < m; ++i)
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T_n *= static_cast<float> (m - i) / static_cast<float> (N - i);
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float T_prime_n = 1.0f;
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std::size_t I_N_best = 0;
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float n = static_cast<float> (m);
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// Define the n_Start coefficients from Section 2.2
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float n_star = static_cast<float> (N);
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float epsilon_n_star = 0.0;
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std::size_t k_n_star = T_N;
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// Compute the I_n_star_min of Equation 8
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std::vector<unsigned int> I_n_star_min (N);
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// Initialize the usual RANSAC parameters
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iterations_ = 0;
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Indices inliers;
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Indices selection;
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Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
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// We will increase the pool so the indices_ vector can only contain m elements at first
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Indices index_pool;
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index_pool.reserve (N);
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for (unsigned int i = 0; i < n; ++i)
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index_pool.push_back (sac_model_->indices_->operator[](i));
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// Iterate
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while (static_cast<unsigned int> (iterations_) < k_n_star)
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{
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// Choose the samples
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// Step 1
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// According to Equation 5 in the text text, not the algorithm
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if ((iterations_ == T_prime_n) && (n < n_star))
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{
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// Increase the pool
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++n;
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if (n >= N)
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break;
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index_pool.push_back (sac_model_->indices_->at(static_cast<unsigned int> (n - 1)));
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// Update other variables
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float T_n_minus_1 = T_n;
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T_n *= (static_cast<float>(n) + 1.0f) / (static_cast<float>(n) + 1.0f - static_cast<float>(m));
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T_prime_n += std::ceil (T_n - T_n_minus_1);
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}
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// Step 2
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sac_model_->indices_->swap (index_pool);
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selection.clear ();
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sac_model_->getSamples (iterations_, selection);
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if (T_prime_n < iterations_)
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{
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selection.pop_back ();
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selection.push_back (sac_model_->indices_->at(static_cast<unsigned int> (n - 1)));
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}
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// Make sure we use the right indices for testing
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sac_model_->indices_->swap (index_pool);
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if (selection.empty ())
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{
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PCL_ERROR ("[pcl::ProgressiveSampleConsensus::computeModel] No samples could be selected!\n");
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break;
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}
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// Search for inliers in the point cloud for the current model
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if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
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{
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++iterations_;
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continue;
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}
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// Select the inliers that are within threshold_ from the model
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inliers.clear ();
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sac_model_->selectWithinDistance (model_coefficients, threshold_, inliers);
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std::size_t I_N = inliers.size ();
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// If we find more inliers than before
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if (I_N > I_N_best)
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{
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I_N_best = I_N;
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// Save the current model/inlier/coefficients selection as being the best so far
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inliers_ = inliers;
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model_ = selection;
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model_coefficients_ = model_coefficients;
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// We estimate I_n_star for different possible values of n_star by using the inliers
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std::sort (inliers.begin (), inliers.end ());
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// Try to find a better n_star
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// We minimize k_n_star and therefore maximize epsilon_n_star = I_n_star / n_star
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std::size_t possible_n_star_best = N, I_possible_n_star_best = I_N;
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float epsilon_possible_n_star_best = static_cast<float>(I_possible_n_star_best) / static_cast<float>(possible_n_star_best);
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// We only need to compute possible better epsilon_n_star for when _n is just about to be removed an inlier
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std::size_t I_possible_n_star = I_N;
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for (auto last_inlier = inliers.crbegin (), inliers_end = inliers.crend ();
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last_inlier != inliers_end;
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++last_inlier, --I_possible_n_star)
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{
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// The best possible_n_star for a given I_possible_n_star is the index of the last inlier
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unsigned int possible_n_star = (*last_inlier) + 1;
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if (possible_n_star <= m)
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break;
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// If we find a better epsilon_n_star
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float epsilon_possible_n_star = static_cast<float>(I_possible_n_star) / static_cast<float>(possible_n_star);
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// Make sure we have a better epsilon_possible_n_star
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if ((epsilon_possible_n_star > epsilon_n_star) && (epsilon_possible_n_star > epsilon_possible_n_star_best))
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{
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// Typo in Equation 7, not (n-m choose i-m) but (n choose i-m)
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std::size_t I_possible_n_star_min = m
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+ static_cast<std::size_t> (std::ceil (boost::math::quantile (boost::math::complement (boost::math::binomial_distribution<float>(static_cast<float> (possible_n_star), 0.1f), 0.05))));
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// If Equation 9 is not verified, exit
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if (I_possible_n_star < I_possible_n_star_min)
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break;
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possible_n_star_best = possible_n_star;
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I_possible_n_star_best = I_possible_n_star;
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epsilon_possible_n_star_best = epsilon_possible_n_star;
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}
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}
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// Check if we get a better epsilon
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if (epsilon_possible_n_star_best > epsilon_n_star)
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{
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// update the best value
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epsilon_n_star = epsilon_possible_n_star_best;
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// Compute the new k_n_star
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float bottom_log = 1 - std::pow (epsilon_n_star, static_cast<float>(m));
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if (bottom_log == 0)
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k_n_star = 1;
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else if (bottom_log == 1)
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k_n_star = T_N;
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else
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k_n_star = static_cast<int> (std::ceil (std::log (0.05) / std::log (bottom_log)));
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// It seems weird to have very few iterations, so do have a few (totally empirical)
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k_n_star = (std::max)(k_n_star, 2 * m);
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}
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}
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++iterations_;
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if (debug_verbosity_level > 1)
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PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] Trial %d out of %d: %d inliers (best is: %d so far).\n", iterations_, k_n_star, I_N, I_N_best);
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if (iterations_ > max_iterations_)
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{
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if (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
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break;
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}
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}
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if (debug_verbosity_level > 0)
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PCL_DEBUG ("[pcl::ProgressiveSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), I_N_best);
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if (model_.empty ())
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{
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inliers_.clear ();
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return (false);
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}
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return (true);
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}
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#define PCL_INSTANTIATE_ProgressiveSampleConsensus(T) template class PCL_EXPORTS pcl::ProgressiveSampleConsensus<T>;
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#endif // PCL_SAMPLE_CONSENSUS_IMPL_PROSAC_H_
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