| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2022 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. |
| // |
| // Authors: joydeepb@cs.utexas.edu (Joydeep Biswas) |
| |
| #include "ceres/fake_bundle_adjustment_jacobian.h" |
| |
| #include <memory> |
| #include <random> |
| #include <string> |
| #include <utility> |
| |
| #include "Eigen/Dense" |
| #include "ceres/block_sparse_matrix.h" |
| #include "ceres/internal/eigen.h" |
| |
| namespace ceres::internal { |
| |
| std::unique_ptr<BlockSparseMatrix> CreateFakeBundleAdjustmentJacobian( |
| int num_cameras, |
| int num_points, |
| int camera_size, |
| int point_size, |
| double visibility, |
| std::mt19937& prng) { |
| constexpr int kResidualSize = 2; |
| |
| CompressedRowBlockStructure* bs = new CompressedRowBlockStructure; |
| int c = 0; |
| // Add column blocks for each point |
| for (int i = 0; i < num_points; ++i) { |
| bs->cols.push_back(Block(point_size, c)); |
| c += point_size; |
| } |
| |
| // Add column blocks for each camera. |
| for (int i = 0; i < num_cameras; ++i) { |
| bs->cols.push_back(Block(camera_size, c)); |
| c += camera_size; |
| } |
| |
| std::bernoulli_distribution visibility_distribution(visibility); |
| int row_pos = 0; |
| int cell_pos = 0; |
| for (int i = 0; i < num_points; ++i) { |
| for (int j = 0; j < num_cameras; ++j) { |
| if (!visibility_distribution(prng)) { |
| continue; |
| } |
| bs->rows.emplace_back(); |
| auto& row = bs->rows.back(); |
| row.block.position = row_pos; |
| row.block.size = kResidualSize; |
| auto& cells = row.cells; |
| cells.resize(2); |
| |
| cells[0].block_id = i; |
| cells[0].position = cell_pos; |
| cell_pos += kResidualSize * point_size; |
| |
| cells[1].block_id = num_points + j; |
| cells[1].position = cell_pos; |
| cell_pos += kResidualSize * camera_size; |
| |
| row_pos += kResidualSize; |
| } |
| } |
| |
| auto jacobian = std::make_unique<BlockSparseMatrix>(bs); |
| VectorRef(jacobian->mutable_values(), jacobian->num_nonzeros()).setRandom(); |
| return jacobian; |
| } |
| |
| std::pair< |
| std::unique_ptr<PartitionedMatrixView<2, Eigen::Dynamic, Eigen::Dynamic>>, |
| std::unique_ptr<BlockSparseMatrix>> |
| CreateFakeBundleAdjustmentPartitionedJacobian(int num_cameras, |
| int num_points, |
| int camera_size, |
| int landmark_size, |
| double visibility, |
| std::mt19937& rng) { |
| using PartitionedView = |
| PartitionedMatrixView<2, Eigen::Dynamic, Eigen::Dynamic>; |
| auto block_sparse_matrix = CreateFakeBundleAdjustmentJacobian( |
| num_cameras, num_points, camera_size, landmark_size, visibility, rng); |
| auto partitioned_view = |
| std::make_unique<PartitionedView>(*block_sparse_matrix, num_points); |
| return std::make_pair(std::move(partitioned_view), |
| std::move(block_sparse_matrix)); |
| } |
| |
| } // namespace ceres::internal |