EssentialKernel.hpp

// This file is part of the AliceVision project.
// Copyright (c) 2016 AliceVision contributors.
// Copyright (c) 2012 openMVG contributors.
// Copyright (c) 2010 libmv contributors.
// This Source Code Form is subject to the terms of the Mozilla Public License,
// v. 2.0. If a copy of the MPL was not distributed with this file,
// You can obtain one at https://mozilla.org/MPL/2.0/.
 
#pragma once
 
#include <aliceVision/robustEstimation/conditioning.hpp>
#include <aliceVision/robustEstimation/PointFittingKernel.hpp>
#include <aliceVision/multiview/relativePose/Essential5PSolver.hpp>
#include <aliceVision/multiview/relativePose/Essential8PSolver.hpp>
#include <aliceVision/multiview/relativePose/FundamentalError.hpp>
#include <aliceVision/multiview/essential.hpp>
#include <aliceVision/multiview/Unnormalizer.hpp>
 
namespace aliceVision {
namespace multiview {
namespace relativePose {
 
template<typename SolverT, typename ErrorT, typename ModelT = robustEstimation::Mat3Model>
class EssentialKernel : public robustEstimation::PointFittingKernel<SolverT, ErrorT, ModelT>
{
  public:
    using KernelBase = robustEstimation::PointFittingKernel<SolverT, ErrorT, ModelT>;
 
    EssentialKernel(const Mat& x1, const Mat& x2, const Mat3& K1, const Mat3& K2)
      : robustEstimation::PointFittingKernel<SolverT, ErrorT, ModelT>(x1, x2),
        _K1(K1),
        _K2(K2)
    {}
 
    void fit(const std::vector<std::size_t>& samples, std::vector<ModelT>& models) const override
    {
        const Mat x1 = buildSubsetMatrix(KernelBase::_x1, samples);
        const Mat x2 = buildSubsetMatrix(KernelBase::_x2, samples);
 
        assert(2 == x1.rows());
        assert(KernelBase::_kernelSolver.getMinimumNbRequiredSamples() <= x1.cols());
        assert(x1.rows() == x2.rows());
        assert(x1.cols() == x2.cols());
 
        // normalize the data (image coords to camera coords).
        const Mat3 K1Inverse = _K1.inverse();
        const Mat3 K2Inverse = _K2.inverse();
 
        Mat x1_normalized;
        Mat x2_normalized;
 
        robustEstimation::applyTransformationToPoints(x1, K1Inverse, &x1_normalized);
        robustEstimation::applyTransformationToPoints(x2, K2Inverse, &x2_normalized);
 
        KernelBase::_kernelSolver.solve(x1_normalized, x2_normalized, models);
    }
 
    double error(std::size_t sample, const ModelT& model) const
    {
        Mat3 F;
        fundamentalFromEssential(model.getMatrix(), _K1, _K2, &F);
        robustEstimation::Mat3Model modelF(F);
        return KernelBase::_errorEstimator.error(modelF, KernelBase::_x1.col(sample), KernelBase::_x2.col(sample));
    }
 
  protected:
    // The two camera calibrated camera matrix
    Mat3 _K1, _K2;
};
 
typedef EssentialKernel<Essential8PSolver, FundamentalSampsonError, robustEstimation::Mat3Model> Essential8PKernel;
 
typedef EssentialKernel<Essential5PSolver, FundamentalSampsonError, robustEstimation::Mat3Model> Essential5PKernel;
 
}  // namespace relativePose
}  // namespace multiview
}  // namespace aliceVision