// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2023 Google Inc. All rights reserved. // http://ceres-solver.org/ // // 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 Google Inc. 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 OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: kushalav@google.com (Avanish Kushal) #include "ceres/visibility.h" #include #include #include #include #include #include #include #include #include "ceres/block_structure.h" #include "ceres/graph.h" #include "ceres/pair_hash.h" #include "glog/logging.h" namespace ceres::internal { void ComputeVisibility(const CompressedRowBlockStructure& block_structure, const int num_eliminate_blocks, std::vector>* visibility) { CHECK(visibility != nullptr); // Clear the visibility vector and resize it to hold a // vector for each camera. visibility->resize(0); visibility->resize(block_structure.cols.size() - num_eliminate_blocks); for (const auto& row : block_structure.rows) { const std::vector& cells = row.cells; int block_id = cells[0].block_id; // If the first block is not an e_block, then skip this row block. if (block_id >= num_eliminate_blocks) { continue; } for (int j = 1; j < cells.size(); ++j) { int camera_block_id = cells[j].block_id - num_eliminate_blocks; DCHECK_GE(camera_block_id, 0); DCHECK_LT(camera_block_id, visibility->size()); (*visibility)[camera_block_id].insert(block_id); } } } std::unique_ptr> CreateSchurComplementGraph( const std::vector>& visibility) { const time_t start_time = time(nullptr); // Compute the number of e_blocks/point blocks. Since the visibility // set for each e_block/camera contains the set of e_blocks/points // visible to it, we find the maximum across all visibility sets. int num_points = 0; for (const auto& visible : visibility) { if (!visible.empty()) { num_points = std::max(num_points, (*visible.rbegin()) + 1); } } // Invert the visibility. The input is a camera->point mapping, // which tells us which points are visible in which // cameras. However, to compute the sparsity structure of the Schur // Complement efficiently, its better to have the point->camera // mapping. std::vector> inverse_visibility(num_points); for (int i = 0; i < visibility.size(); i++) { const std::set& visibility_set = visibility[i]; for (int v : visibility_set) { inverse_visibility[v].insert(i); } } // Map from camera pairs to number of points visible to both cameras // in the pair. std::unordered_map, int, pair_hash> camera_pairs; // Count the number of points visible to each camera/f_block pair. for (const auto& inverse_visibility_set : inverse_visibility) { for (auto camera1 = inverse_visibility_set.begin(); camera1 != inverse_visibility_set.end(); ++camera1) { auto camera2 = camera1; for (++camera2; camera2 != inverse_visibility_set.end(); ++camera2) { ++(camera_pairs[std::make_pair(*camera1, *camera2)]); } } } auto graph = std::make_unique>(); // Add vertices and initialize the pairs for self edges so that self // edges are guaranteed. This is needed for the Canonical views // algorithm to work correctly. static constexpr double kSelfEdgeWeight = 1.0; for (int i = 0; i < visibility.size(); ++i) { graph->AddVertex(i); graph->AddEdge(i, i, kSelfEdgeWeight); } // Add an edge for each camera pair. for (const auto& camera_pair_count : camera_pairs) { const int camera1 = camera_pair_count.first.first; const int camera2 = camera_pair_count.first.second; const int count = camera_pair_count.second; DCHECK_NE(camera1, camera2); // Static cast necessary for Windows. const double weight = static_cast(count) / (sqrt(static_cast(visibility[camera1].size() * visibility[camera2].size()))); graph->AddEdge(camera1, camera2, weight); } VLOG(2) << "Schur complement graph time: " << (time(nullptr) - start_time); return graph; } } // namespace ceres::internal