| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
| // All rights reserved. | |
| // | |
| // Redistribution and use in source and binary forms, with or without | |
| // modification, are permitted provided that the following conditions are met: | |
| // | |
| // * Redistributions of source code must retain the above copyright | |
| // notice, this list of conditions and the following disclaimer. | |
| // | |
| // * Redistributions in binary form must reproduce the above copyright | |
| // notice, this list of conditions and the following disclaimer in the | |
| // documentation and/or other materials provided with the distribution. | |
| // | |
| // * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of | |
| // its contributors may be used to endorse or promote products derived | |
| // from this software without specific prior written permission. | |
| // | |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE | |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE | |
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| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS | |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN | |
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| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE | |
| // POSSIBILITY OF SUCH DAMAGE. | |
| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| ProgressiveSampler::ProgressiveSampler(const size_t num_samples) | |
| : num_samples_(num_samples), | |
| total_num_samples_(0), | |
| t_(0), | |
| n_(0), | |
| T_n_(0), | |
| T_n_p_(0) {} | |
| void ProgressiveSampler::Initialize(const size_t total_num_samples) { | |
| CHECK_LE(num_samples_, total_num_samples); | |
| total_num_samples_ = total_num_samples; | |
| t_ = 0; | |
| n_ = num_samples_; | |
| // Number of iterations before PROSAC behaves like RANSAC. Default value | |
| // is chosen according to the recommended value in the paper. | |
| const size_t kNumProgressiveIterations = 200000; | |
| // Compute T_n using recurrent relation in equation 3 (first part). | |
| T_n_ = kNumProgressiveIterations; | |
| T_n_p_ = 1.0; | |
| for (size_t i = 0; i < num_samples_; ++i) { | |
| T_n_ *= static_cast<double>(num_samples_ - i) / (total_num_samples_ - i); | |
| } | |
| } | |
| size_t ProgressiveSampler::MaxNumSamples() { | |
| return std::numeric_limits<size_t>::max(); | |
| } | |
| std::vector<size_t> ProgressiveSampler::Sample() { | |
| t_ += 1; | |
| // Compute T_n_p_ using recurrent relation in equation 3 (second part). | |
| if (t_ == T_n_p_ && n_ < total_num_samples_) { | |
| const double T_n_plus_1 = T_n_ * (n_ + 1.0) / (n_ + 1.0 - num_samples_); | |
| T_n_p_ += std::ceil(T_n_plus_1 - T_n_); | |
| T_n_ = T_n_plus_1; | |
| n_ += 1; | |
| } | |
| // Decide how many samples to draw from which part of the data as | |
| // specified in equation 5. | |
| size_t num_random_samples = num_samples_; | |
| size_t max_random_sample_idx = n_ - 1; | |
| if (T_n_p_ >= t_) { | |
| num_random_samples -= 1; | |
| max_random_sample_idx -= 1; | |
| } | |
| // Draw semi-random samples as described in algorithm 1. | |
| std::vector<size_t> sampled_idxs; | |
| sampled_idxs.reserve(num_samples_); | |
| for (size_t i = 0; i < num_random_samples; ++i) { | |
| while (true) { | |
| const size_t random_idx = | |
| RandomInteger<uint32_t>(0, max_random_sample_idx); | |
| if (!VectorContainsValue(sampled_idxs, random_idx)) { | |
| sampled_idxs.push_back(random_idx); | |
| break; | |
| } | |
| } | |
| } | |
| // In progressive sampling mode, the last element is mandatory. | |
| if (T_n_p_ >= t_) { | |
| sampled_idxs.push_back(n_); | |
| } | |
| return sampled_idxs; | |
| } | |
| } // namespace colmap | |