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

Supported functionality

Basic operations:

  • General dimension kd-trees (using bundled nanoflann)
  • Surface normal and curvature 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) and Eigen matrices
  • RGBD image pair to/from point cloud conversion utilities

Convex hulls:

  • A general dimension convex polytope representation that is computed (using bundled Qhull) 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


  • General dimension k-means clustering that supports all distance metrics supported by nanoflann
  • Spectral clustering based on various graph Laplacian types (using bundled 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 alignment under the point-to-point metric (general dimension), point-to-plane metric (3D), or any combination thereof
    • Non-rigid alignment of 3D point sets, by means of a robustly regularized, locally rigid warp field, under any combination of the point-to-point and point-to-plane metrics; implementations for both densely and sparsely (similarly to DynamicFusion) 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


  • Classical Multidimensional Scaling (using bundled Spectra for eigendecompositions)
  • A powerful, extensible, and easy to use 3D visualizer



cilantro is developed and tested on Ubuntu 14.04, 16.04, and 18.04 variants using CMake. Please note that you may have to manually set up a recent version of Eigen on Ubuntu 14.04, as the one provided in the official repos is outdated. To clone and build the library (with bundled examples), execute the following in a terminal:

git clone https://github.com/kzampog/cilantro.git
cd cilantro
mkdir build
cd build
cmake ..
make -j


Documentation (readthedocs.io, Doxygen API documentation) 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.


The library is released under the MIT license. If you use cilantro in your research, please cite our technical report:

    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}