203 lines
6.7 KiB
C++
203 lines
6.7 KiB
C++
#include "PointCloud.hpp"
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#include "libslic3r/Geometry.hpp"
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#include "libslic3r/Tesselate.hpp"
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#include <igl/random_points_on_mesh.h>
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namespace Slic3r { namespace branchingtree {
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std::optional<Vec3f> find_merge_pt(const Vec3f &A,
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const Vec3f &B,
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float max_slope)
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{
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Vec3f Da = (B - A).normalized(), Db = -Da;
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auto [polar_da, azim_da] = Geometry::dir_to_spheric(Da);
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auto [polar_db, azim_db] = Geometry::dir_to_spheric(Db);
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polar_da = std::max(polar_da, float(PI) / 2.f + max_slope);
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polar_db = std::max(polar_db, float(PI) / 2.f + max_slope);
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Da = Geometry::spheric_to_dir<float>(polar_da, azim_da);
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Db = Geometry::spheric_to_dir<float>(polar_db, azim_db);
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// This formula is based on
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// https://stackoverflow.com/questions/27459080/given-two-points-and-two-direction-vectors-find-the-point-where-they-intersect
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double t1 =
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(A.z() * Db.x() + Db.z() * B.x() - B.z() * Db.x() - Db.z() * A.x()) /
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(Da.x() * Db.z() - Da.z() * Db.x());
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if (std::isnan(t1) || std::abs(t1) < EPSILON)
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t1 = (A.z() * Db.y() + Db.z() * B.y() - B.z() * Db.y() - Db.z() * A.y()) /
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(Da.y() * Db.z() - Da.z() * Db.y());
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Vec3f m1 = A + t1 * Da;
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double t2 = (m1.z() - B.z()) / Db.z();
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return t1 >= 0. && t2 >= 0. ? m1 : std::optional<Vec3f>{};
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}
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void to_eigen_mesh(const indexed_triangle_set &its,
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Eigen::MatrixXd &V,
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Eigen::MatrixXi &F)
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{
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V.resize(its.vertices.size(), 3);
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F.resize(its.indices.size(), 3);
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for (unsigned int i = 0; i < its.indices.size(); ++i)
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F.row(i) = its.indices[i];
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for (unsigned int i = 0; i < its.vertices.size(); ++i)
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V.row(i) = its.vertices[i].cast<double>();
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}
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std::vector<Node> sample_mesh(const indexed_triangle_set &its, double radius)
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{
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std::vector<Node> ret;
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double surface_area = 0.;
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for (const Vec3i &face : its.indices) {
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std::array<Vec3f, 3> tri = {its.vertices[face(0)],
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its.vertices[face(1)],
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its.vertices[face(2)]};
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auto U = tri[1] - tri[0], V = tri[2] - tri[0];
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surface_area += 0.5 * U.cross(V).norm();
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}
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int N = surface_area / (PI * radius * radius);
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Eigen::MatrixXd B;
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Eigen::MatrixXi FI;
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Eigen::MatrixXd V;
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Eigen::MatrixXi F;
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to_eigen_mesh(its, V, F);
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igl::random_points_on_mesh(N, V, F, B, FI);
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ret.reserve(size_t(N));
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for (int i = 0; i < FI.size(); i++) {
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int face_id = FI(i);
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if (face_id < 0 || face_id >= int(its.indices.size()))
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continue;
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Vec3i face = its.indices[face_id];
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if (face(0) >= int(its.vertices.size()) ||
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face(1) >= int(its.vertices.size()) ||
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face(2) >= int(its.vertices.size()))
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continue;
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Vec3f c = B.row(i)(0) * its.vertices[face(0)] +
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B.row(i)(1) * its.vertices[face(1)] +
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B.row(i)(2) * its.vertices[face(2)];
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ret.emplace_back(c);
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}
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return ret;
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}
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std::vector<Node> sample_bed(const ExPolygons &bed, float z, double radius)
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{
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std::vector<Vec3f> ret;
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auto triangles = triangulate_expolygons_3d(bed, z);
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indexed_triangle_set its;
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its.vertices.reserve(triangles.size());
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for (size_t i = 0; i < triangles.size(); i += 3) {
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its.vertices.emplace_back(triangles[i].cast<float>());
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its.vertices.emplace_back(triangles[i + 1].cast<float>());
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its.vertices.emplace_back(triangles[i + 2].cast<float>());
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its.indices.emplace_back(i, i + 1, i + 2);
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}
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return sample_mesh(its, radius);
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}
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PointCloud::PointCloud(const indexed_triangle_set &M,
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std::vector<Node> support_leafs,
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const Properties &props)
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: PointCloud{sample_mesh(M, props.sampling_radius()),
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sample_bed(props.bed_shape(),
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props.ground_level(),
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props.sampling_radius()),
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std::move(support_leafs), props}
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{}
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PointCloud::PointCloud(std::vector<Node> meshpts,
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std::vector<Node> bedpts,
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std::vector<Node> support_leafs,
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const Properties &props)
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: m_leafs{std::move(support_leafs)}
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, m_meshpoints{std::move(meshpts)}
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, m_bedpoints{std::move(bedpts)}
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, m_props{props}
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, cos2bridge_slope{std::cos(props.max_slope()) *
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std::abs(std::cos(props.max_slope()))}
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, MESHPTS_BEGIN{m_bedpoints.size()}
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, LEAFS_BEGIN{MESHPTS_BEGIN + m_meshpoints.size()}
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, JUNCTIONS_BEGIN{LEAFS_BEGIN + m_leafs.size()}
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, m_searchable_indices(JUNCTIONS_BEGIN, true)
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, m_queue_indices(JUNCTIONS_BEGIN, UNQUEUED)
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, m_reachable_cnt{JUNCTIONS_BEGIN}
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, m_ktree{CoordFn{this}, LEAFS_BEGIN} // Only for bed and mesh points
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{
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for (size_t i = 0; i < m_bedpoints.size(); ++i)
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m_bedpoints[i].id = int(i);
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for (size_t i = 0; i < m_meshpoints.size(); ++i)
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m_meshpoints[i].id = int(MESHPTS_BEGIN + i);
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for (size_t i = 0; i < m_leafs.size(); ++i)
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m_leafs[i].id = int(LEAFS_BEGIN + i);
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}
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float PointCloud::get_distance(const Vec3f &p, size_t node_id) const
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{
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auto t = get_type(node_id);
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auto ret = std::numeric_limits<float>::infinity();
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const auto &node = get(node_id);
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switch (t) {
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case MESH:
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case BED: {
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// Points of mesh or bed which are outside of the support cone of
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// 'pos' must be discarded.
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if (is_outside_support_cone(p, node.pos))
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ret = std::numeric_limits<float>::infinity();
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else
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ret = (node.pos - p).norm();
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break;
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}
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case LEAF:
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case JUNCTION:{
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auto mergept = find_merge_pt(p, node.pos, m_props.max_slope());
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double maxL2 = m_props.max_branch_length() * m_props.max_branch_length();
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if (!mergept || mergept->z() < (m_props.ground_level() + 2 * node.Rmin))
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ret = std::numeric_limits<float>::infinity();
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else if (double a = (node.pos - *mergept).squaredNorm(),
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b = (p - *mergept).squaredNorm();
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a < maxL2 && b < maxL2)
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ret = std::sqrt(b);
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break;
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}
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case NONE:
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;
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}
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// Setting the ret val to infinity will effectively discard this
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// connection of nodes. max_branch_length property is used here
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// to discard node=>node and node=>mesh connections longer than this
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// property.
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if (t != BED && ret > m_props.max_branch_length())
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ret = std::numeric_limits<float>::infinity();
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return ret;
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}
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}} // namespace Slic3r::branchingtree
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