cloudcompare.2.11.3

 # nanoflann [](https://travis-ci.org/jlblancoc/nanoflann) ## 1. About *nanoflann* is a **C++11 [header-only](http://en.wikipedia.org/wiki/Header-only) library** for building KD-Trees of datasets with different topologies: R², R³ (point clouds), SO(2) and SO(3) (2D and 3D rotation groups). No support for approximate NN is provided. *nanoflann* does not require compiling or installing. You just need to `#include <nanoflann.hpp>` in your code. This library is a *fork* of the [flann library](http://www.cs.ubc.ca/research/flann/) ([git](https://github.com/mariusmuja/flann)) by Marius Muja and David G. Lowe, and born as a child project of [MRPT](https://www.mrpt.org/). Following the original license terms, *nanoflann* is distributed under the BSD license. Please, for bugs use the issues button or fork and open a pull request. Cite as: ``` @misc{blanco2014nanoflann, title = {nanoflann: a {C}++ header-only fork of {FLANN}, a library for Nearest Neighbor ({NN}) with KD-trees}, author = {Blanco, Jose Luis and Rai, Pranjal Kumar}, howpublished = {\url{https://github.com/jlblancoc/nanoflann}}, year = {2014} } ``` ### 1.1. Obtaining the code * Easiest way: clone this GIT repository and take the `include/nanoflann.hpp` file for use where you need it. * macOS users can install `nanoflann` with [Homebrew](https://brew.sh) with: ```shell $ brew install brewsci/science/nanoflann ``` or ```shell $ brew tap brewsci/science $ brew install nanoflann ``` * Linux users can install it with [Linuxbrew](https://linuxbrew.sh/) with: `brew install homebrew/science/nanoflann` * List of [**stable releases**](https://github.com/jlblancoc/nanoflann/releases). Check out the [CHANGELOG](https://raw.githubusercontent.com/jlblancoc/nanoflann/master/CHANGELOG.txt) Although nanoflann itself doesn't have to be compiled, you can build some examples and tests with: sudo apt-get install build-essential cmake libgtest-dev libeigen3-dev mkdir build && cd build && cmake .. make && make test ### 1.2. C++ API reference * Browse the [Doxygen documentation](http://jlblancoc.github.io/nanoflann/). * **Important note:** If L2 norms are used, notice that search radius and all passed and returned distances are actually *squared distances*. ### 1.3. Code examples * KD-tree look-up with `kdd_search()` and `radius_search()`: [pointcloud_kdd_radius.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp) * KD-tree look-up on a point cloud dataset: [pointcloud_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_example.cpp) * KD-tree look-up on a dynamic point cloud dataset: [dynamic_pointcloud_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/dynamic_pointcloud_example.cpp) * KD-tree look-up on a rotation group (SO2): [SO2_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/SO2_adaptor_example.cpp) * KD-tree look-up on a rotation group (SO3): [SO3_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/SO3_adaptor_example.cpp) * KD-tree look-up on a point cloud dataset with an external adaptor class: [pointcloud_adaptor_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_adaptor_example.cpp) * KD-tree look-up directly on an `Eigen::Matrix<>`: [matrix_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/matrix_example.cpp) * KD-tree look-up directly on `std::vector<std::vector<T> >` or `std::vector<Eigen::VectorXd>`: [vector_of_vectors_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/vector_of_vectors_example.cpp) * Example with a `Makefile` for usage through `pkg-config` (for example, after doing a "make install" or after installing from Ubuntu repositories): [example_with_pkgconfig/](https://github.com/jlblancoc/nanoflann/blob/master/examples/example_with_pkgconfig/) * Example of how to build an index and save it to disk for later usage: [saveload_example.cpp](https://github.com/jlblancoc/nanoflann/blob/master/examples/saveload_example.cpp) ### 1.4. Why a fork? * **Execution time efficiency**: * The power of the original `flann` library comes from the possibility of choosing between different ANN algorithms. The cost of this flexibility is the declaration of pure virtual methods which (in some circumstances) impose [run-time penalties](http://www.cs.cmu.edu/~gilpin/c%2B%2B/performance.html#virtualfunctions). In `nanoflann` all those virtual methods have been replaced by a combination of the [Curiously Recurring Template Pattern](http://en.wikipedia.org/wiki/Curiously_recurring_template_pattern) (CRTP) and inlined methods, which are much faster. * For `radiusSearch()`, there is no need to make a call to determine the number of points within the radius and then call it again to get the data. By using STL containers for the output data, containers are automatically resized. * Users can (optionally) set the problem dimensionality at compile-time via a template argument, thus allowing the compiler to fully unroll loops. * `nanoflann` allows users to provide a precomputed bounding box of the data, if available, to avoid recomputation. * Indices of data points have been converted from `int` to `size_t`, which removes a limit when handling very large data sets. * **Memory efficiency**: Instead of making a copy of the entire dataset into a custom `flann`-like matrix before building a KD-tree index, `nanoflann` allows direct access to your data via an **adaptor interface** which must be implemented in your class. Refer to the examples below or to the C++ API of [nanoflann::KDTreeSingleIndexAdaptor<>](http://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html) for more info. ### 1.5. What can *nanoflann* do? * Building KD-trees with a single index (no randomized KD-trees, no approximate searches). * Fast, thread-safe querying for closest neighbors on KD-trees. The entry points are: * [nanoflann::KDTreeSingleIndexAdaptor<>](http://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::knnSearch()` * Finds the `num_closest` nearest neighbors to `query_point[0:dim-1]`. Their indices are stored inside the result object. See an [example usage code](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp#L119). * [nanoflann::KDTreeSingleIndexAdaptor<>](http://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::radiusSearch()` * Finds all the neighbors to `query_point[0:dim-1]` within a maximum radius. The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance. See an [example usage code](https://github.com/jlblancoc/nanoflann/blob/master/examples/pointcloud_kdd_radius.cpp#L141). * [nanoflann::KDTreeSingleIndexAdaptor<>](http://jlblancoc.github.io/nanoflann/classnanoflann_1_1KDTreeSingleIndexAdaptor.html)`::radiusSearchCustomCallback()` * Can be used to receive a callback for each point found in range. This may be more efficient in some situations instead of building a huge vector of pairs with the results. * Working with 2D and 3D point clouds or N-dimensional data sets. * Working directly with `Eigen::Matrix<>` classes (matrices and vectors-of-vectors). * Working with dynamic point clouds without a need to rebuild entire kd-tree index. * Working with the distance metrics: * `R^N`: Euclidean spaces: * `L1` (Manhattan) * `L2` (**squared** Euclidean norm, favoring SSE2 optimization). * `L2_Simple` (**squared** Euclidean norm, for low-dimensionality data sets like point clouds).