fix: Delaunay3D circumcenter 完整实现 + 测试恢复

bench/bench_spatial.cpp

@@ -0,0 +1,113 @@
+/// bench_spatial.cpp — R-Tree / KD-Tree build + kNN benchmarks
+///
+/// Metrics:
+///   RTree_Build/N{size}    — STR-bulk-build time for N random 3D points
+///   RTree_kNN/N{size}      — kNN(n=10) queries on a pre-built R-Tree
+///   KDTree_Build/N{size}   — build time for N random 3D points
+///   KDTree_kNN/N{size}     — kNN(n=10) queries on a pre-built KD-Tree
+
+#include <benchmark/benchmark.h>
+#include <vde/spatial/r_tree.h>
+#include <vde/spatial/kd_tree.h>
+#include <vde/core/point.h>
+#include <random>
+#include <vector>
+
+using namespace vde;
+
+namespace {
+
+std::vector<core::Point3D> random_points_3d(int n, double R = 100.0) {
+    std::mt19937 rng(42);
+    std::uniform_real_distribution<double> dist(-R, R);
+    std::vector<core::Point3D> pts;
+    pts.reserve(n);
+    for (int i = 0; i < n; ++i)
+        pts.emplace_back(dist(rng), dist(rng), dist(rng));
+    return pts;
+}
+
+/// 20 random query points for kNN
+std::vector<core::Point3D> query_points(int n = 20, double R = 100.0) {
+    return random_points_3d(n, R);
+}
+
+} // namespace
+
+// ── R-Tree ────────────────────────────────────────────────────────────
+
+static void RTree_Build(benchmark::State& state) {
+    auto pts = random_points_3d(state.range(0));
+    for (auto _ : state) {
+        spatial::RTree<core::Point3D> tree;
+        tree.build(pts);
+        benchmark::DoNotOptimize(tree.size());
+    }
+    state.SetItemsProcessed(state.iterations() * state.range(0));
+}
+BENCHMARK(RTree_Build)->Arg(1000)->Arg(10000)->Arg(100000);
+
+class RTreeFixture : public benchmark::Fixture {
+public:
+    void SetUp(const benchmark::State& state) override {
+        points = random_points_3d(state.range(0));
+        tree.build(points);
+        queries = query_points();
+    }
+
+    spatial::RTree<core::Point3D> tree;
+    std::vector<core::Point3D> points;
+    std::vector<core::Point3D> queries;
+};
+
+BENCHMARK_DEFINE_F(RTreeFixture, RTree_kNN)(benchmark::State& state) {
+    size_t total = 0;
+    for (auto _ : state) {
+        for (const auto& q : queries) {
+            auto result = tree.query_knn(q, 10);
+            total += result.size();
+        }
+    }
+    benchmark::DoNotOptimize(total);
+    state.SetItemsProcessed(state.iterations() * queries.size());
+}
+BENCHMARK_REGISTER_F(RTreeFixture, RTree_kNN)->Arg(1000)->Arg(10000)->Arg(100000);
+
+// ── KD-Tree ──────────────────────────────────────────────────────────
+
+static void KDTree_Build(benchmark::State& state) {
+    auto pts = random_points_3d(state.range(0));
+    for (auto _ : state) {
+        spatial::KDTree<core::Point3D> tree;
+        tree.build(pts);
+        benchmark::DoNotOptimize(tree.size());
+    }
+    state.SetItemsProcessed(state.iterations() * state.range(0));
+}
+BENCHMARK(KDTree_Build)->Arg(1000)->Arg(10000)->Arg(100000);
+
+class KDTreeFixture : public benchmark::Fixture {
+public:
+    void SetUp(const benchmark::State& state) override {
+        points = random_points_3d(state.range(0));
+        tree.build(points);
+        queries = query_points();
+    }
+
+    spatial::KDTree<core::Point3D> tree;
+    std::vector<core::Point3D> points;
+    std::vector<core::Point3D> queries;
+};
+
+BENCHMARK_DEFINE_F(KDTreeFixture, KDTree_kNN)(benchmark::State& state) {
+    size_t total = 0;
+    for (auto _ : state) {
+        for (const auto& q : queries) {
+            auto result = tree.query_knn(q, 10);
+            total += result.size();
+        }
+    }
+    benchmark::DoNotOptimize(total);
+    state.SetItemsProcessed(state.iterations() * queries.size());
+}
+BENCHMARK_REGISTER_F(KDTreeFixture, KDTree_kNN)->Arg(1000)->Arg(10000)->Arg(100000);