cilantro is a lean C++ library for working with point cloud data.

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:

Convex hulls:


Geometric registration:

Robust model estimation:




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you may have to manually set up a recent version of Eigen on Ubuntu 14.04, as the one provided in the official repos is outdated.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
cd cilantro
mkdir build
cd build
cmake ..
make -j


Documentation (, 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}