sdf_optimize.h

浏览该文件的文档.

#pragma once
#include "vde/sdf/sdf_tree.h"
#include "vde/core/point.h"
#include <vector>
#include <functional>
 
namespace vde::sdf {
 
using core::Point3D;
using core::Vector3D;
 
// ────────────────────────────────────────────────
//  Shape Optimization
// ────────────────────────────────────────────────
 
struct OptimizeResult {
    SdfNodePtr optimized_shape;      
    double final_loss;               
    int iterations;                  
    bool converged;                  
    std::vector<double> loss_history; 
};
 
using LossFn = std::function<double(const std::function<double(const Point3D&)>&)>;
 
// ── Shape Fitting ───────────────────────────────
 
[[nodiscard]] OptimizeResult fit_to_point_cloud(
    const SdfNodePtr& initial,
    const std::vector<Point3D>& target_points,
    double learning_rate = 0.01,
    int max_iterations = 100);
 
[[nodiscard]] OptimizeResult fit_surface_to_points(
    const SdfNodePtr& initial,
    const std::vector<Point3D>& surface_points,
    double learning_rate = 0.01,
    int max_iterations = 100);
 
[[nodiscard]] OptimizeResult fit_to_sdf(
    const SdfNodePtr& source,
    const std::function<double(const Point3D&)>& target_sdf,
    const Point3D& bmin, const Point3D& bmax,
    int grid_resolution = 16,
    double learning_rate = 0.01,
    int max_iterations = 100);
 
// ── Collision Avoidance ─────────────────────────
 
[[nodiscard]] bool resolve_collision(
    SdfNodePtr& shape_a, SdfNodePtr& shape_b,
    double step_size = 0.1,
    int max_iterations = 50);
 
[[nodiscard]] double penetration_depth(
    const SdfNodePtr& shape_a,
    const SdfNodePtr& shape_b,
    const Point3D& bmin, const Point3D& bmax,
    int samples = 1000);
 
// ── Reachability / Accessibility ─────────────────
 
[[nodiscard]] double accessibility(
    const SdfNodePtr& shape,
    const Point3D& p,           // surface point
    const Vector3D& direction,  // approach direction
    int hemisphere_samples = 64);
 
[[nodiscard]] Point3D find_accessible_point(
    const SdfNodePtr& shape,
    const Vector3D& approach_dir,
    const Point3D& bmin, const Point3D& bmax,
    int grid_res = 32);
 
// ── Symmetry Detection ──────────────────────────
 
[[nodiscard]] double symmetry_score(
    const SdfNodePtr& shape,
    const Point3D& bmin, const Point3D& bmax,
    const Vector3D& plane_normal,
    double plane_offset = 0.0,
    int samples = 1000);
 
struct SymmetryResult {
    double score;      
    Vector3D normal;   
    double offset;     
};
 
[[nodiscard]] SymmetryResult find_symmetry_plane(
    const SdfNodePtr& shape,
    const Point3D& bmin, const Point3D& bmax,
    int samples = 1000);
 
// ── SDF Volume Computation ──────────────────────
 
[[nodiscard]] double estimate_volume(
    const SdfNodePtr& shape,
    const Point3D& bmin, const Point3D& bmax,
    int samples = 10000);
 
[[nodiscard]] Point3D center_of_mass(
    const SdfNodePtr& shape,
    const Point3D& bmin, const Point3D& bmax,
    int samples = 10000);
 
// ── Parameter Collection ────────────────────────
 
struct ParamRef {
    double* value;     
    std::string name;  
};
 
[[nodiscard]] std::vector<ParamRef> collect_params(SdfNodePtr& root);
 
[[nodiscard]] std::vector<double> numerical_gradient(
    const std::vector<ParamRef>& params,
    const std::function<double()>& loss_fn,
    double eps = 1e-6);
 
// ── Gradient Descent Utility ────────────────────
 
class GradientDescent {
public:
    explicit GradientDescent(double lr) : lr_(lr) {}
 
    double step(std::vector<double>& params,
                const std::function<double(const std::vector<double>&,
                                           std::vector<double>&)>& grad_fn);
 
    void set_learning_rate(double lr) { lr_ = lr; }
 
    [[nodiscard]] double learning_rate() const { return lr_; }
 
    [[nodiscard]] int iteration() const { return iteration_; }
 
private:
    double lr_;
    int iteration_ = 0;
};
 
} // namespace vde::sdf