59991e54f5
Brings a lot of performance improvements and bug fixes. Keyframe selection in bundle-adjustment.blend goes down from 4.5 seconds to 3.0 on M2 Ultra. The reconstruction itself stays within 0.2 seconds. Full change log can be found at http://ceres-solver.org/version_history.html Pull Request: https://projects.blender.org/blender/blender/pulls/136896
121 lines
4.4 KiB
C++
121 lines
4.4 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2023 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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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 are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN 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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// Authors: joydeepb@cs.utexas.edu (Joydeep Biswas)
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#include "ceres/fake_bundle_adjustment_jacobian.h"
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#include <memory>
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#include <random>
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#include <string>
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#include <utility>
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#include "Eigen/Dense"
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/internal/eigen.h"
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namespace ceres::internal {
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std::unique_ptr<BlockSparseMatrix> CreateFakeBundleAdjustmentJacobian(
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int num_cameras,
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int num_points,
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int camera_size,
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int point_size,
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double visibility,
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std::mt19937& prng) {
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constexpr int kResidualSize = 2;
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CompressedRowBlockStructure* bs = new CompressedRowBlockStructure;
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int c = 0;
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// Add column blocks for each point
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for (int i = 0; i < num_points; ++i) {
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bs->cols.push_back(Block(point_size, c));
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c += point_size;
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}
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// Add column blocks for each camera.
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for (int i = 0; i < num_cameras; ++i) {
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bs->cols.push_back(Block(camera_size, c));
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c += camera_size;
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}
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std::bernoulli_distribution visibility_distribution(visibility);
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int row_pos = 0;
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int cell_pos = 0;
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for (int i = 0; i < num_points; ++i) {
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for (int j = 0; j < num_cameras; ++j) {
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if (!visibility_distribution(prng)) {
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continue;
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}
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bs->rows.emplace_back();
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auto& row = bs->rows.back();
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row.block.position = row_pos;
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row.block.size = kResidualSize;
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auto& cells = row.cells;
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cells.resize(2);
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cells[0].block_id = i;
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cells[0].position = cell_pos;
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cell_pos += kResidualSize * point_size;
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cells[1].block_id = num_points + j;
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cells[1].position = cell_pos;
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cell_pos += kResidualSize * camera_size;
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row_pos += kResidualSize;
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}
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}
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auto jacobian = std::make_unique<BlockSparseMatrix>(bs);
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VectorRef(jacobian->mutable_values(), jacobian->num_nonzeros()).setRandom();
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return jacobian;
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}
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std::pair<
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std::unique_ptr<PartitionedMatrixView<2, Eigen::Dynamic, Eigen::Dynamic>>,
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std::unique_ptr<BlockSparseMatrix>>
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CreateFakeBundleAdjustmentPartitionedJacobian(int num_cameras,
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int num_points,
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int camera_size,
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int landmark_size,
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double visibility,
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std::mt19937& rng) {
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using PartitionedView =
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PartitionedMatrixView<2, Eigen::Dynamic, Eigen::Dynamic>;
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auto block_sparse_matrix = CreateFakeBundleAdjustmentJacobian(
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num_cameras, num_points, camera_size, landmark_size, visibility, rng);
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LinearSolver::Options options;
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options.elimination_groups.push_back(num_points);
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auto partitioned_view =
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std::make_unique<PartitionedView>(options, *block_sparse_matrix);
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return std::make_pair(std::move(partitioned_view),
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std::move(block_sparse_matrix));
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}
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} // namespace ceres::internal
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