Benhabiles, HalimLavoué, GuillaumeVandeborre, Jean‐PhilippeDaoudi, MohamedEduard Groeller and Holly Rushmeier2015-02-272015-02-2720111467-8659https://doi.org/10.1111/j.1467-8659.2011.01967.xThis paper presents a 3D‐mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D‐meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state‐of‐the‐art.