Examples

README.md

@@ -46,14 +46,14 @@ sudo make install
 #### Install from [PyPI](https://pypi.org/project/small-gicp/)
 
 ```bash
-pip install small_gicp --user
+pip install small_gicp
 ```
 
 #### Install from source
 
 ```bash
 cd small_gicp
-pip install . --user
+pip install .
 
 # [Optional (linux)] Install stubs for autocomplete (If you know a better way, let me know...)
 pip install pybind11-stubgen
@@ -404,6 +404,31 @@ open3d.visualization.draw_geometries([target_o3d, source_o3d])
 
 - [Scan-to-scan and scan-to-model GICP matching odometry on KITTI](src/example/kitti_odometry.py)
 
+## Running examples
+
+### C++
+
+```bash
+cd small_gicp
+mkdir build && cd build
+cmake .. -DBUILD_EXAMPLES=ON && make -j
+
+cd ..
+./build/01_basic_registration
+./build/02_basic_registration_pcl
+./build/03_registration_template
+```
+
+
+### Python
+
+```bash
+cd small_gicp
+pip install .
+
+python3 src/example/basic_registration.py
+```
+
 ## [Benchmark](BENCHMARK.md)
 
 Processing speed comparison between small_gicp and Open3D ([youtube]((https://youtu.be/LNESzGXPr4c?feature=shared))).

docs/Doxyfile

@@ -917,7 +917,7 @@ RECURSIVE              = YES
 # Note that relative paths are relative to the directory from which doxygen is
 # run.
 
-EXCLUDE                = include/small_gicp/benchmark
+EXCLUDE                = ../include/small_gicp/benchmark/
 
 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
 # directories that are symbolic links (a Unix file system feature) are excluded

include/small_gicp/ann/traits.hpp

@@ -16,14 +16,15 @@ struct Traits;
 /// @param tree       Nearest neighbor search (e.g., KdTree)
 /// @param point      Query point
 /// @param k          Number of neighbors
-/// @param k_index    [out] Index of the nearest neighbor
-/// @param k_sq_dist  [out] Squared distance to the nearest neighbor
+/// @param k_indices  [out] Indices of k-nearest neighbors
+/// @param k_sq_dists [out] Squared distances to k-nearest neighbors
 /// @return Number of found neighbors
 template <typename T>
 size_t knn_search(const T& tree, const Eigen::Vector4d& point, size_t k, size_t* k_indices, double* k_sq_dists) {
   return Traits<T>::knn_search(tree, point, k, k_indices, k_sq_dists);
 }
 
+/// @brief Check if T has nearest_neighbor_search method.
 template <typename T>
 struct has_nearest_neighbor_search {
   template <typename U, int = (&Traits<U>::nearest_neighbor_search, 0)>
@@ -33,7 +34,7 @@ struct has_nearest_neighbor_search {
   static constexpr bool value = decltype(test((T*)nullptr))::value;
 };
 
-/// @brief Find the nearest neighbor.
+/// @brief Find the nearest neighbor. If Traits<T>::nearest_neighbor_search is not defined, fallback to knn_search with k=1.
 /// @param tree       Nearest neighbor search (e.g., KdTree)
 /// @param point      Query point
 /// @param k_index    [out] Index of the nearest neighbor

src/example/basic_registration.py

@@ -9,6 +9,8 @@
 
 # Basic registation example with numpy arrays
 def example_numpy1(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
+  print('*** example_numpy1 ***')
+
   # Example A : Perform registration with numpy arrays
   # Arguments
   # - target_points               : Nx4 or Nx3 numpy array of the target point cloud
@@ -22,10 +24,15 @@ def example_numpy1(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.nd
   # - num_threads                 : Number of threads
   result = small_gicp.align(target_raw_numpy, source_raw_numpy, downsampling_resolution=0.25)
 
+  print('--- registration result ---')
+  print(result)
+
   return result.T_target_source
 
 # Example to perform preprocessing and registration separately
 def example_numpy2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
+  print('*** example_numpy2 ***')
+
   # Example B : Perform preprocessing and registration separately
 
   # Preprocess point clouds
@@ -38,6 +45,9 @@ def example_numpy2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.nd
   target, target_tree = small_gicp.preprocess_points(target_raw_numpy, downsampling_resolution=0.25)
   source, source_tree = small_gicp.preprocess_points(source_raw_numpy, downsampling_resolution=0.25)
 
+  print('preprocessed target=', target)
+  print('preprocessed source=', source)
+
   # Align point clouds
   # Arguments
   # - target                      : Target point cloud (small_gicp.PointCloud)
@@ -48,26 +58,39 @@ def example_numpy2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.nd
   # - max_correspondence_distance : Maximum correspondence distance
   # - num_threads                 : Number of threads
   result = small_gicp.align(target, source, target_tree)
-
+
+  print('--- registration result ---')
+  print(result)
+
   return result.T_target_source
 
 
 # Basic registation example with small_gicp.PointCloud
 def example_small1(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
+  print('*** example_small1 ***')
+
   # Convert numpy arrays (Nx3 or Nx4) to small_gicp.PointCloud
   target_raw = small_gicp.PointCloud(target_raw_numpy)
   source_raw = small_gicp.PointCloud(source_raw_numpy)
 
   # Preprocess point clouds
   target, target_tree = small_gicp.preprocess_points(target_raw, downsampling_resolution=0.25)
   source, source_tree = small_gicp.preprocess_points(source_raw, downsampling_resolution=0.25)
-
+
+  print('preprocessed target=', target)
+  print('preprocessed source=', source)
+
   result = small_gicp.align(target, source, target_tree)
+
+  print('--- registration result ---')
+  print(result)
 
   return result.T_target_source
 
 # Example to perform each preprocessing and registration separately
 def example_small2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.ndarray):
+  print('*** example_small2 ***')
+
   # Convert numpy arrays (Nx3 or Nx4) to small_gicp.PointCloud
   target_raw = small_gicp.PointCloud(target_raw_numpy)
   source_raw = small_gicp.PointCloud(source_raw_numpy)
@@ -79,13 +102,19 @@ def example_small2(target_raw_numpy : numpy.ndarray, source_raw_numpy : numpy.nd
   # KdTree construction
   target_tree = small_gicp.KdTree(target)
   source_tree = small_gicp.KdTree(source)
-  
+
   # Estimate covariances
   small_gicp.estimate_covariances(target, target_tree)
   small_gicp.estimate_covariances(source, source_tree)
 
+  print('preprocessed target=', target)
+  print('preprocessed source=', source)
+
   # Align point clouds  
   result = small_gicp.align(target, source, target_tree)
+
+  print('--- registration result ---')
+  print(result)
 
   return result.T_target_source