\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.13.2 - Classification
Bibliography
[1]

Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:2001, 2001.

[2]

ETH Zurich Random Forest Template Library. Stefan Walk (ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Geodesy and Photogrammetry), 2014.

[3]

Timo Hackel, Jan D Wegner, and Konrad Schindler. Fast semantic segmentation of 3d point clouds with strongly varying density. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, 3:177–184, 2016.

[4]

Florent Lafarge and Clement Mallet. Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. International Journal of Computer Vision, 99(1):69–85, 2012.

[5]

Stan Z. Li. Markov Random Field Modeling in Image Analysis. Springer Publishing Company, Incorporated, 3rd edition, 2009.