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https://github.com/kzampog/cilantro

A lean C++ library for working with point cloud data
https://github.com/kzampog/cilantro

3d 3d-visualization clustering convex convex-hull icp iterative-closest-point k-means mds mean-shift model-fitting non-rigid-registration pca point-cloud ransac reconstruction registration rgbd segmentation spectral-clustering

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A lean C++ library for working with point cloud data

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## A Lean and Efficient Library for Point Cloud Data Processing

`cilantro` is a lean and fast C++ library for working with point cloud data, with emphasis given to the 3D case.

It includes efficient implementations for a variety of common operations, providing a clean API and attempting to minimize the amount of boilerplate code.

The library is extensively templated, enabling operations on data of arbitrary numerical type and dimensionality (where applicable) and featuring a modular/extensible design of the more complex procedures.

At the same time, convenience aliases/wrappers for the most common cases are provided.

A high-level description of `cilantro` can be found in our [technical report](https://arxiv.org/abs/1807.00399).



## Supported functionality

#### Basic operations:

- General dimension kd-trees (using bundled [nanoflann](https://github.com/jlblancoc/nanoflann))

- Surface normal and curvature (robust) estimation from raw point clouds

- General dimension grid-based point cloud resampling

- Principal Component Analysis

- Basic I/O utilities for 3D point clouds (in PLY format, using bundled [tinyply](https://github.com/ddiakopoulos/tinyply)) and Eigen matrices

- RGBD image pair to/from point cloud conversion utilities



#### Convex hulls and spatial reasoning:

- A general dimension convex polytope representation that is computed (using bundled [Qhull](http://www.qhull.org/)) from either vertex or half-space intersection input and allows for easy switching between the respective representations

- A representation of generic (general dimension) space regions as unions of convex polytopes that implements set operations



    




#### Clustering:

- General dimension k-means clustering that supports all distance metrics supported by [nanoflann](https://github.com/jlblancoc/nanoflann)

- Spectral clustering based on various graph Laplacian types (using bundled [Spectra](https://github.com/yixuan/spectra))

- Mean-shift clustering with custom kernel support

- Connected component based point cloud segmentation that supports arbitrary point-wise similarity functions



    




#### Geometric registration:

- Multiple generic Iterative Closest Point implementations that support arbitrary correspondence search methods in arbitrary point feature spaces for:

    - **Rigid** or **affine** alignment under the point-to-point metric (general dimension), point-to-plane metric (2D or 3D), or any combination thereof

    - **Non-rigid** alignment of 2D or 3D point sets, by means of a robustly regularized, **locally-rigid** or **locally-affine** deformation field, under any combination of the point-to-point and point-to-plane metrics; implementations for both *densely* and *sparsely* (by means of an Embedded Deformation Graph) supported warp fields are provided



    

    


    




#### Robust model estimation:

- A RANSAC estimator template and instantiations thereof for general dimension:

    - Robust hyperplane estimation

    - Rigid point cloud registration given noisy correspondences



#### Visualization:

- Classical Multidimensional Scaling (using bundled [Spectra](https://github.com/yixuan/spectra) for eigendecompositions)

- A powerful, extensible, and easy to use 3D visualizer



## Dependencies

- [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) (version 3.3 or newer) [**required**]

- [Pangolin](https://github.com/stevenlovegrove/Pangolin) (built with Eigen enabled) [**optional**; needed for visualization modules and most examples]



## Building

`cilantro` is developed and tested on Ubuntu variants (18.04 and newer) using [CMake](https://cmake.org/).

To clone and build the library (with bundled examples), execute the following in a terminal:



```bash

git clone https://github.com/kzampog/cilantro.git

cd cilantro

mkdir build

cd build

cmake ..

make -j

```



## Documentation

Documentation ([readthedocs.io](http://cilantro.readthedocs.io/en/latest/?badge=latest), [Doxygen API reference](https://codedocs.xyz/kzampog/cilantro/)) is a work in progress.

The short provided examples (built by default) cover a significant part of the library's functionality.

Most of them expect a single command-line argument (path to a point cloud file in PLY format).

One such input is bundled in `examples/test_clouds` for quick testing.



## License

The library is released under the [MIT license](https://github.com/kzampog/cilantro/blob/master/LICENSE).

If you use `cilantro` in your research, please cite our [technical report](https://arxiv.org/abs/1807.00399):

```bib

@inproceedings{zampogiannis2018cilantro,

    author = {Zampogiannis, Konstantinos and Fermuller, Cornelia and Aloimonos, Yiannis},

    title = {cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data Processing},

    booktitle = {Proceedings of the 26th ACM International Conference on Multimedia},

    series = {MM '18},

    year = {2018},

    isbn = {978-1-4503-5665-7},

    location = {Seoul, Republic of Korea},

    pages = {1364--1367},

    doi = {10.1145/3240508.3243655},

    publisher = {ACM},

    address = {New York, NY, USA}

}

```