blob: dd2aee8493b3a238211d250b488cc8bb2ba20eaa [file] [log] [blame]
// 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 <iostream>
#include <memory>
#include <random>
#include <string>
#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