cnc.hpp

 
#pragma once
 
#include <random>
#include <numeric>
 
namespace Ponca::internal
{
    template <TriangleGenerationMethod Method, typename P>
    struct TriangleGenerator
    {
        using VectorType = typename P::VectorType;
        template <typename IndexRange, typename PointContainer, typename NeighborFilter>
        static FIT_RESULT generate(const IndexRange& /*ids*/, const PointContainer& /*points*/,
                                   const NeighborFilter& /*w*/, std::vector<Triangle<P>>& /*triangles*/
        )
        {
            throw std::invalid_argument("Triangle generation method not implemented!");
        }
    };
 
    template <typename P>
    struct TriangleGenerator<UniformGeneration, P>
    {
    private:
        static constexpr int maxTriangles{100};
 
    public:
        using VectorType = typename P::VectorType;
        using Scalar     = typename P::Scalar;
 
        template <typename IndexRange, typename PointContainer, typename NeighborFilter>
        static FIT_RESULT generate(const IndexRange& ids, const PointContainer& points, const NeighborFilter& w,
                                   std::vector<Triangle<P>>& triangles)
        {
            // Makes a new array
            std::vector<int> indices;
            for (int index : ids)
            {
                // Skip the points that are outside the kernel radius
                if (w(points[index]).first == Scalar(0.))
                    continue;
                indices.push_back(index);
            }
            if (indices.empty())
                return UNDEFINED;
 
            const int lastIndex = int(indices.size()) - 1;
            for (int i = 0; i < maxTriangles; ++i)
            {
                // Randomly select triangles
                int i1 = indices[Eigen::internal::random<int>(0, lastIndex)];
                int i2 = indices[Eigen::internal::random<int>(0, lastIndex)];
                int i3 = indices[Eigen::internal::random<int>(0, lastIndex)];
                if (i1 == i2 || i1 == i3 || i2 == i3)
                    continue;
 
                triangles.push_back(internal::Triangle<P>(points[i1], points[i2], points[i3]));
            }
            return STABLE;
        }
    };
 
    template <typename P>
    struct TriangleGenerator<IndependentGeneration, P>
    {
    private:
        static constexpr int maxTriangles{100};
 
    public:
        using VectorType = typename P::VectorType;
        using Scalar     = typename P::Scalar;
 
        template <typename IndexRange, typename PointContainer, typename NeighborFilter>
        static FIT_RESULT generate(const IndexRange& ids, const PointContainer& points, const NeighborFilter& w,
                                   std::vector<Triangle<P>>& triangles)
        {
            // Makes a new array to shuffle
            std::vector<int> indices;
            for (int index : ids)
            {
                // Skip the points that are outside the kernel radius
                if (w(points[index]).first == Scalar(0.))
                    continue;
                indices.push_back(index);
            }
            if (indices.empty())
                return UNDEFINED;
 
            // Shuffles the neighbors
            std::random_device rd;
            std::mt19937 rg(rd());
            std::shuffle(indices.begin(), indices.end(), rg);
 
            // Compute the triangles
            triangles.clear();
            const int max_triangles = std::min(maxTriangles, int(ids.size() / 3));
            for (int nb_vt = 0; nb_vt < max_triangles - 2; nb_vt++)
            {
                int i1 = indices[nb_vt];
                int i2 = indices[nb_vt + 1];
                int i3 = indices[nb_vt + 2];
                triangles.push_back(internal::Triangle<P>(points[i1], points[i2], points[i3]));
            }
            return STABLE;
        }
    };
 
    template <typename P>
    struct HexagramBase
    {
        using Scalar = typename P::Scalar;
        static constexpr Scalar avg_normal_coef{Scalar(0.5)};
    };
 
    template <typename P>
    struct TriangleGenerator<HexagramGeneration, P> : protected HexagramBase<P>
    {
        using VectorType = typename P::VectorType;
        using Scalar     = typename P::Scalar;
 
        template <typename IndexRange, typename PointContainer, typename NeighborFilter>
        static FIT_RESULT generate(const IndexRange& ids, const PointContainer& points, const NeighborFilter& w,
                                   std::vector<Triangle<P>>& triangles)
        {
            PONCA_MULTIARCH_STD_MATH(abs);
            // Compute normal and maximum distance.
            VectorType c = w.evalPos();
            VectorType n = w.evalNormal();
            VectorType a{VectorType::Zero()};
            Scalar avg_d = Scalar(0);
 
            bool isUndefined       = true;
            int valid_points_count = 0;
            for (int index : ids)
            {
                // Skip the points that are outside the kernel radius
                if (w(points[index]).first == Scalar(0.))
                    continue;
                auto p = points[index];
                avg_d += (p.pos() - c).norm();
                a += p.normal();
                isUndefined = false;
                valid_points_count++;
            }
            if (isUndefined)
                return UNDEFINED;
 
            a /= a.norm();
            n = (Scalar(1) - HexagramBase<P>::avg_normal_coef) * n + HexagramBase<P>::avg_normal_coef * a;
            n /= n.norm();
            avg_d /= valid_points_count;
 
            // Define basis for sector analysis.
            const int m = abs(n[0]) > abs(n[1]) ? (abs(n[0]) > abs(n[2]) ? 0 : 2) : (abs(n[1]) > abs(n[2]) ? 1 : 2);
 
            const VectorType e = (m == 0) ? VectorType(0, 1, 0) : (m == 1) ? VectorType(0, 0, 1) : VectorType(1, 0, 0);
 
            VectorType u = n.cross(e);
            VectorType v = n.cross(u);
            u /= u.norm();
            v /= v.norm();
 
            std::array<VectorType, 6> positions;
            std::array<VectorType, 6> normals;
            std::array<Scalar, 6> distance2;
            std::array<VectorType, 6> targets;
 
            for (int i = 0; i < 6; i++)
            {
                distance2[i] = avg_d * avg_d;
                targets[i]   = avg_d * (u * cos(i * M_PI / 3.0) + v * sin(i * M_PI / 3.0));
                positions[i] = w.evalPos();
                normals[i]   = w.evalNormal();
            }
 
            // Compute closest points.
            for (int index : ids)
            {
                // Skip the points that are outside the kernel radius
                if (w(points[index]).first == Scalar(0.))
                    continue;
 
                VectorType p = points[index].pos();
                if (p == c)
                    continue; // Skip the eval point
                const VectorType d = p - c;
 
                for (int j = 0; j < 6; j++)
                {
                    const Scalar d2 = (d - targets[j]).squaredNorm();
                    if (d2 < distance2[j])
                    {
                        distance2[j] = d2;
                        positions[j] = p;
                        normals[j]   = points[index].normal();
                    }
                }
            }
            triangles.push_back(internal::Triangle<P>({positions[0], positions[2], positions[4]},
                                                      {normals[0], normals[2], normals[4]}));
            triangles.push_back(internal::Triangle<P>({positions[1], positions[3], positions[5]},
                                                      {normals[1], normals[3], normals[5]}));
 
            return STABLE;
        }
    };
 
    template <typename P>
    struct TriangleGenerator<AvgHexagramGeneration, P> : protected HexagramBase<P>
    {
        using VectorType = typename P::VectorType;
        using Scalar     = typename P::Scalar;
 
        template <typename IndexRange, typename PointContainer, typename NeighborFilter>
        static FIT_RESULT generate(const IndexRange& ids, const PointContainer& points, const NeighborFilter& w,
                                   std::vector<Triangle<P>>& triangles)
        {
            // Compute normal and maximum distance.
            VectorType c = w.evalPos();
            VectorType n = w.evalNormal();
            VectorType a = VectorType::Zero();
            Scalar avg_d = Scalar(0);
 
            std::array<VectorType, 6> targets;
            Scalar avg_normal = Scalar(0.5);
 
            bool isUndefined       = true;
            int valid_points_count = 0;
            for (int index : ids)
            {
                if (w(points[index]).first == Scalar(0.))
                    continue; // Skip the points that are outside the kernel radius
                a += points[index].normal();
                avg_d += (points[index].pos() - c).norm();
                isUndefined = false;
                valid_points_count++;
            }
            if (isUndefined)
                return UNDEFINED;
 
            a /= a.norm();
            n = (Scalar(1) - HexagramBase<P>::avg_normal_coef) * n + HexagramBase<P>::avg_normal_coef * a;
            n /= n.norm();
            avg_d /= valid_points_count;
 
            // Define basis for sector analysis.
            const int m = (std::abs(n[0]) > std::abs(n[1])) ? ((std::abs(n[0])) > std::abs(n[2]) ? 0 : 2)
                                                            : ((std::abs(n[1])) > std::abs(n[2]) ? 1 : 2);
 
            const VectorType e = (m == 0) ? VectorType(0, 1, 0) : (m == 1) ? VectorType(0, 0, 1) : VectorType(1, 0, 0);
            VectorType u       = n.cross(e);
            VectorType v       = n.cross(u);
            u /= u.norm();
            v /= v.norm();
 
            // Initialize the average values
            std::array<VectorType, 6> array_avg_normals;
            std::array<VectorType, 6> array_avg_pos;
            std::array<int, 6> array_nb{};
            for (int i = 0; i < 6; i++)
            {
                targets[i]           = avg_d * (u * cos(i * M_PI / 3.0) + v * sin(i * M_PI / 3.0));
                array_avg_normals[i] = VectorType::Zero();
                array_avg_pos[i]     = VectorType::Zero();
            }
 
            // Compute closest points.
            for (int index : ids)
            {
                if (w(points[index]).first == Scalar(0.))
                    continue; // Skip the points that are outside the kernel radius
                VectorType p   = points[index].pos() - c;
                int best_k     = 0;
                Scalar best_d2 = (p - targets[0]).squaredNorm();
                for (int k = 1; k < 6; k++)
                {
                    const Scalar d2 = (p - targets[k]).squaredNorm();
                    if (d2 < best_d2)
                    {
                        best_k  = k;
                        best_d2 = d2;
                    }
                }
                array_avg_normals[best_k] += points[index].normal();
                array_avg_pos[best_k] += points[index].pos();
                array_nb[best_k] += 1;
            }
 
            for (int i = 0; i < 6; i++)
            {
                if (array_nb[i] == 0)
                {
                    array_avg_normals[i] = w.evalNormal();
                    array_avg_pos[i]     = w.evalPos();
                }
                else
                {
                    array_avg_normals[i] /= array_avg_normals[i].norm();
                    array_avg_pos[i] /= Scalar(array_nb[i]);
                }
            }
 
            triangles.push_back(
                internal::Triangle<P>({array_avg_pos[0], array_avg_pos[2], array_avg_pos[4]},
                                      {array_avg_normals[0], array_avg_normals[2], array_avg_normals[4]}));
            triangles.push_back(
                internal::Triangle<P>({array_avg_pos[1], array_avg_pos[3], array_avg_pos[5]},
                                      {array_avg_normals[1], array_avg_normals[3], array_avg_normals[5]}));
            return STABLE;
        }
    };
} // namespace Ponca::internal
 
namespace Ponca
{
    template <class P, TriangleGenerationMethod M>
    template <typename PointContainer>
    FIT_RESULT CNC<P, M>::compute(const PointContainer& points)
    {
        init();
        std::vector<unsigned int> indicesSample(points.size());
        std::iota(indicesSample.begin(), indicesSample.end(), 0);
 
        m_eCurrentState = internal::TriangleGenerator<M, P>::generate(indicesSample, points, m_nFilter, m_triangles);
        if (m_eCurrentState != STABLE)
            return m_eCurrentState;
        m_nb_vt = int(m_triangles.size());
        return finalize();
    }
 
    template <class P, TriangleGenerationMethod M>
    template <typename IndexRange, typename PointContainer>
    FIT_RESULT CNC<P, M>::computeWithIds(const IndexRange& ids, const PointContainer& points)
    {
        init();
        m_eCurrentState = internal::TriangleGenerator<M, P>::generate(ids, points, m_nFilter, m_triangles);
        if (m_eCurrentState != STABLE)
            return m_eCurrentState;
        m_nb_vt = int(m_triangles.size());
        return finalize();
    }
 
    template <class P, TriangleGenerationMethod M>
    FIT_RESULT CNC<P, M>::finalize()
    {
        m_A = Scalar(0);
        m_H = Scalar(0);
        m_G = Scalar(0);
 
        MatrixType localT = MatrixType::Zero();
 
        for (int t = 0; t < m_nb_vt; ++t)
        {
            // Simple estimation.
            Scalar tA = m_triangles[t].mu0InterpolatedU();
            if (tA < -internal::CNCEigen<P>::epsilon)
            {
                m_A -= tA;
                m_H += m_triangles[t].template mu1InterpolatedU<true>();
                m_G += m_triangles[t].template mu2InterpolatedU<true>();
                localT += m_triangles[t].template muXYInterpolatedU<true>();
            }
            else if (tA > internal::CNCEigen<P>::epsilon)
            {
                m_A += tA;
                m_H += m_triangles[t].mu1InterpolatedU();
                m_G += m_triangles[t].mu2InterpolatedU();
                localT += m_triangles[t].muXYInterpolatedU();
            }
        } // end for t
 
        m_T11 = localT(0, 0);
        m_T12 = Scalar(0.5) * (localT(0, 1) + localT(1, 0));
        m_T13 = Scalar(0.5) * (localT(0, 2) + localT(2, 0));
        m_T22 = localT(1, 1);
        m_T23 = Scalar(0.5) * (localT(1, 2) + localT(2, 1));
        m_T33 = localT(2, 2);
 
        MatrixType T;
        if (m_A != Scalar(0))
        {
            T << m_T11, m_T12, m_T13, m_T12, m_T22, m_T23, m_T13, m_T23, m_T33;
            T /= m_A;
            m_H /= m_A;
            m_G /= m_A;
        }
        else
        {
            m_H = Scalar(0);
            m_G = Scalar(0);
        }
 
        std::tie(m_k2, m_k1, m_v2, m_v1) = internal::CNCEigen<P>::curvaturesFromTensor(T, 1.0, m_nFilter.evalNormal());
 
        return STABLE;
    }
} // namespace Ponca