Single cloud benchmark unrealistic. Updated to use two.

Author: Jaybro

examples/benchmark/CMakeLists.txt

@@ -51,7 +51,6 @@ endif()
 add_executable(uosr_to_bin)
 target_sources(uosr_to_bin
     PRIVATE
-        ${CMAKE_CURRENT_LIST_DIR}/format_uosr.cpp
         ${CMAKE_CURRENT_LIST_DIR}/uosr_to_bin.cpp
 )
 target_link_libraries(uosr_to_bin PRIVATE pico_toolshed)

examples/benchmark/benchmark.hpp

@@ -9,18 +9,20 @@ namespace pico_tree {
 class Benchmark : public benchmark::Fixture {
  protected:
   using Index = int;
-  using Scalar = double;
-  using PointX = Point3d;
+  using Scalar = float;
+  using PointX = Point3f;
 
  public:
   Benchmark() {
-    // Here you may need to be patient. Depending on the size of the compiled
-    // binary.
-    pico_tree::ReadBin("./scans.bin", &points_);
+    // Here you may need to be patient depending on the size of the binaries.
+    // Loaded for each benchmark.
+    pico_tree::ReadBin("./scans0.bin", &points_tree_);
+    pico_tree::ReadBin("./scans1.bin", &points_test_);
   }
 
  protected:
-  std::vector<PointX> points_;
+  std::vector<PointX> points_tree_;
+  std::vector<PointX> points_test_;
 };
 
 }  // namespace pico_tree

examples/benchmark/bin_to_ascii.cpp

@@ -15,7 +15,7 @@ int main() {
     std::cout << path_bin.string() << " doesn't exist." << std::endl;
   } else if (!std::filesystem::exists(path_ascii)) {
     std::cout << "Reading points in bin format..." << std::endl;
-    std::vector<Point3d> points;
+    std::vector<Point3f> points;
     pico_tree::ReadBin(path_bin.string(), &points);
     std::cout << "Read " << points.size() << " points." << std::endl;
     std::cout << "Writing points to ascii xyz format..." << std::endl;

examples/benchmark/bm_nanoflann.cpp

@@ -26,7 +26,7 @@ class BmNanoflann : public pico_tree::Benchmark {
 
 BENCHMARK_DEFINE_F(BmNanoflann, BuildCt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
-  NanoAdaptorX adaptor(points_);
+  NanoAdaptorX adaptor(points_tree_);
   for (auto _ : state) {
     NanoKdTreeCt<NanoAdaptorX> tree(
         PointX::Dim,
@@ -38,7 +38,7 @@ BENCHMARK_DEFINE_F(BmNanoflann, BuildCt)(benchmark::State& state) {
 
 BENCHMARK_DEFINE_F(BmNanoflann, BuildRt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
-  NanoAdaptorX adaptor(points_);
+  NanoAdaptorX adaptor(points_tree_);
   for (auto _ : state) {
     NanoKdTreeRt<NanoAdaptorX> tree(
         PointX::Dim,
@@ -66,7 +66,7 @@ BENCHMARK_REGISTER_F(BmNanoflann, BuildRt)
 BENCHMARK_DEFINE_F(BmNanoflann, KnnCt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
   int knn_count = state.range(1);
-  NanoAdaptorX adaptor(points_);
+  NanoAdaptorX adaptor(points_tree_);
   NanoKdTreeCt<NanoAdaptorX> tree(
       PointX::Dim,
       adaptor,
@@ -77,7 +77,7 @@ BENCHMARK_DEFINE_F(BmNanoflann, KnnCt)(benchmark::State& state) {
     std::vector<Index> indices(knn_count);
     std::vector<Scalar> distances(knn_count);
     std::size_t sum = 0;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       benchmark::DoNotOptimize(
           sum +=
           tree.knnSearch(p.data, knn_count, indices.data(), distances.data()));
@@ -120,9 +120,9 @@ BENCHMARK_REGISTER_F(BmNanoflann, KnnCt)
 
 BENCHMARK_DEFINE_F(BmNanoflann, RadiusCt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
-  double radius = static_cast<double>(state.range(1)) / 10.0;
-  double squared = radius * radius;
-  NanoAdaptorX adaptor(points_);
+  Scalar radius = static_cast<Scalar>(state.range(1)) / 10.0;
+  Scalar squared = radius * radius;
+  NanoAdaptorX adaptor(points_tree_);
   NanoKdTreeCt<NanoAdaptorX> tree(
       PointX::Dim,
       adaptor,
@@ -132,7 +132,7 @@ BENCHMARK_DEFINE_F(BmNanoflann, RadiusCt)(benchmark::State& state) {
   for (auto _ : state) {
     std::vector<std::pair<Index, Scalar>> results;
     std::size_t sum = 0;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       benchmark::DoNotOptimize(
           sum += tree.radiusSearch(
               p.data, squared, results, nanoflann::SearchParams{0, 0, false}));

examples/benchmark/bm_opencv_flann.cpp

@@ -19,7 +19,8 @@ class BmOpenCvFlann : public pico_tree::Benchmark {
 
 BENCHMARK_DEFINE_F(BmOpenCvFlann, BuildRt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
-  cvflann::Matrix<Scalar> matrix(points_.data()->data, points_.size(), 3);
+  cvflann::Matrix<Scalar> matrix(
+      points_tree_.data()->data, points_tree_.size(), 3);
   // Reorder will change the order of the input to fit the generated indices,
   // but it will replace (delete) the original input.
   cvflann::KDTreeSingleIndexParams pindex(max_leaf_size, false);
@@ -43,7 +44,8 @@ BENCHMARK_DEFINE_F(BmOpenCvFlann, KnnRt)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
   int knn_count = state.range(1);
 
-  cvflann::Matrix<Scalar> matrix(points_.data()->data, points_.size(), 3);
+  cvflann::Matrix<Scalar> matrix(
+      points_tree_.data()->data, points_tree_.size(), 3);
   // Reorder will change the order of the input to fit the generated indices,
   // but it will replace (and delete) the original input. Note that the reorder
   // option does not appear to have effect on the performance of the queries.
@@ -68,7 +70,7 @@ BENCHMARK_DEFINE_F(BmOpenCvFlann, KnnRt)(benchmark::State& state) {
     cvflann::Matrix<Index> mat_indices(indices.data(), 1, knn_count);
     cvflann::Matrix<Scalar> mat_distances(distances.data(), 1, knn_count);
 
-    for (auto& p : points_) {
+    for (auto& p : points_test_) {
       cvflann::Matrix<Scalar> query(p.data, 1, 3);
       tree.knnSearch(query, mat_indices, mat_distances, knn_count, psearch);
     }

examples/benchmark/bm_pico_cover_tree.cpp

@@ -24,7 +24,7 @@ BENCHMARK_DEFINE_F(BmPicoCoverTree, BuildCt)(benchmark::State& state) {
   Scalar base = static_cast<Scalar>(state.range(0)) / Scalar(10.0);
 
   for (auto _ : state) {
-    PicoCoverTree<PointX> tree(points_, base);
+    PicoCoverTree<PointX> tree(points_tree_, base);
   }
 }
 
@@ -41,19 +41,19 @@ BENCHMARK_DEFINE_F(BmPicoCoverTree, KnnCt)(benchmark::State& state) {
   Scalar base = static_cast<Scalar>(state.range(0)) / Scalar(10.0);
   int knn_count = state.range(1);
 
-  PicoCoverTree<PointX> tree(points_, base);
+  PicoCoverTree<PointX> tree(points_tree_, base);
 
   for (auto _ : state) {
     std::vector<pico_tree::Neighbor<Index, Scalar>> results;
     std::size_t sum = 0;
     std::size_t group = 4000;
     std::size_t pi = 0;
-    while (pi < points_.size()) {
-      std::size_t group_end = std::min(pi + group, points_.size());
+    while (pi < points_test_.size()) {
+      std::size_t group_end = std::min(pi + group, points_test_.size());
       std::flush(std::cout);
       ScopedTimer timer("query_group");
       for (; pi < group_end; ++pi) {
-        auto const& p = points_[pi];
+        auto const& p = points_test_[pi];
         tree.SearchKnn(p, knn_count, &results);
         benchmark::DoNotOptimize(sum += results.size());
       }

examples/benchmark/bm_pico_kd_tree.cpp

@@ -43,31 +43,31 @@ BENCHMARK_DEFINE_F(BmPicoKdTree, BuildCtSldMid)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
   for (auto _ : state) {
-    PicoKdTreeCtSldMid<PointX> tree(points_, max_leaf_size);
+    PicoKdTreeCtSldMid<PointX> tree(points_tree_, max_leaf_size);
   }
 }
 
 BENCHMARK_DEFINE_F(BmPicoKdTree, BuildCtLngMed)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
   for (auto _ : state) {
-    PicoKdTreeCtLngMed<PointX> tree(points_, max_leaf_size);
+    PicoKdTreeCtLngMed<PointX> tree(points_tree_, max_leaf_size);
   }
 }
 
 BENCHMARK_DEFINE_F(BmPicoKdTree, BuildRtSldMid)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
   for (auto _ : state) {
-    PicoKdTreeRtSldMid<PointX> tree(points_, max_leaf_size);
+    PicoKdTreeRtSldMid<PointX> tree(points_tree_, max_leaf_size);
   }
 }
 
 BENCHMARK_DEFINE_F(BmPicoKdTree, BuildRtLngMed)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
   for (auto _ : state) {
-    PicoKdTreeRtLngMed<PointX> tree(points_, max_leaf_size);
+    PicoKdTreeRtLngMed<PointX> tree(points_tree_, max_leaf_size);
   }
 }
 
@@ -98,12 +98,12 @@ BENCHMARK_DEFINE_F(BmPicoKdTree, KnnCtSldMid)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
   int knn_count = state.range(1);
 
-  PicoKdTreeCtSldMid<PointX> tree(points_, max_leaf_size);
+  PicoKdTreeCtSldMid<PointX> tree(points_tree_, max_leaf_size);
 
   for (auto _ : state) {
     std::vector<pico_tree::Neighbor<Index, Scalar>> results;
     std::size_t sum = 0;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       tree.SearchKnn(p, knn_count, &results);
       benchmark::DoNotOptimize(sum += results.size());
     }
@@ -114,12 +114,12 @@ BENCHMARK_DEFINE_F(BmPicoKdTree, KnnCtLngMed)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
   int knn_count = state.range(1);
 
-  PicoKdTreeCtLngMed<PointX> tree(points_, max_leaf_size);
+  PicoKdTreeCtLngMed<PointX> tree(points_tree_, max_leaf_size);
 
   for (auto _ : state) {
     std::vector<pico_tree::Neighbor<Index, Scalar>> results;
     std::size_t sum = 0;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       tree.SearchKnn(p, knn_count, &results);
       benchmark::DoNotOptimize(sum += results.size());
     }
@@ -129,11 +129,11 @@ BENCHMARK_DEFINE_F(BmPicoKdTree, KnnCtLngMed)(benchmark::State& state) {
 BENCHMARK_DEFINE_F(BmPicoKdTree, NnCtSldMid)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
-  PicoKdTreeCtSldMid<PointX> tree(points_, max_leaf_size);
+  PicoKdTreeCtSldMid<PointX> tree(points_tree_, max_leaf_size);
 
   for (auto _ : state) {
     pico_tree::Neighbor<Index, Scalar> result;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       tree.SearchNn(p, &result);
     }
   }
@@ -142,11 +142,11 @@ BENCHMARK_DEFINE_F(BmPicoKdTree, NnCtSldMid)(benchmark::State& state) {
 BENCHMARK_DEFINE_F(BmPicoKdTree, NnCtLngMed)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
 
-  PicoKdTreeCtLngMed<PointX> tree(points_, max_leaf_size);
+  PicoKdTreeCtLngMed<PointX> tree(points_tree_, max_leaf_size);
 
   for (auto _ : state) {
     pico_tree::Neighbor<Index, Scalar> result;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       tree.SearchNn(p, &result);
     }
   }
@@ -217,15 +217,15 @@ BENCHMARK_REGISTER_F(BmPicoKdTree, NnCtLngMed)
 
 BENCHMARK_DEFINE_F(BmPicoKdTree, RadiusCtSldMid)(benchmark::State& state) {
   int max_leaf_size = state.range(0);
-  double radius = static_cast<double>(state.range(1)) / 10.0;
-  double squared = radius * radius;
+  Scalar radius = static_cast<Scalar>(state.range(1)) / 10.0;
+  Scalar squared = radius * radius;
 
-  PicoKdTreeCtSldMid<PointX> tree(points_, max_leaf_size);
+  PicoKdTreeCtSldMid<PointX> tree(points_tree_, max_leaf_size);
 
   for (auto _ : state) {
     std::vector<pico_tree::Neighbor<Index, Scalar>> results;
     std::size_t sum = 0;
-    for (auto const& p : points_) {
+    for (auto const& p : points_test_) {
       tree.SearchRadius(p, squared, &results);
       benchmark::DoNotOptimize(sum += results.size());
     }