Method for detecting repetitive structures in 3D mesh models, comprising steps of a) generating sampling points from the 3D mesh model by sampling said 3D mesh model using a current sampling step size; b) clustering said sampling points into a set of sampling point clusters according to curvature descriptors associated with said sampling points; c) for each sampling point cluster: ∘ pairing the sampling points, a first and a second sampling points being paired when a value representative of the curvature descriptor associated with each of said first and second sampling point is invariant under a determined transformation, ∘ determining a transformation space that comprises a set of transformations used to pair said sampling points, ∘ clustering said transformations of said transformation space into a set of transformation clusters, ∘ detecting, within each transformation cluster, one or more repetitive structures from the pairs of sampling points associated with said each transformation cluster and determining a representative for each of the one or more repetitive structures, d) reducing the current sampling step size to obtain a reduced sampling step size, e) sampling each determined representative of each of the one or more detected repetitive structures and a remaining portion of the model having no detected repetitive structure using the reduced sampling step size to obtain sampling points; and f) repeating steps b) to e) unless no more repetitive structures are detected, or a predetermined minimum sampling step size or time-out is reached. a) generating sampling points from the 3D mesh model by sampling said 3D mesh model using a current sampling step size; b) clustering said sampling points into a set of sampling point clusters according to curvature descriptors associated with said sampling points; c) for each sampling point cluster: ∘ pairing the sampling points, a first and a second sampling points being paired when a value representative of the curvature descriptor associated with each of said first and second sampling point is invariant under a determined transformation, ∘ determining a transformation space that comprises a set of transformations used to pair said sampling points, ∘ clustering said transformations of said transformation space into a set of transformation clusters, ∘ detecting, within each transformation cluster, one or more repetitive structures from the pairs of sampling points associated with said each transformation cluster and determining a representative for each of the one or more repetitive structures, ∘ pairing the sampling points, a first and a second sampling points being paired when a value representative of the curvature descriptor associated with each of said first and second sampling point is invariant under a determined transformation, ∘ determining a transformation space that comprises a set of transformations used to pair said sampling points, ∘ clustering said transformations of said transformation space into a set of transformation clusters, ∘ detecting, within each transformation cluster, one or more repetitive structures from the pairs of sampling points associated with said each transformation cluster and determining a representative for each of the one or more repetitive structures, d) reducing the current sampling step size to obtain a reduced sampling step size, e) sampling each determined representative of each of the one or more detected repetitive structures and a remaining portion of the model having no detected repetitive structure using the reduced sampling step size to obtain sampling points; and f) repeating steps b) to e) unless no more repetitive structures are detected, or a predetermined minimum sampling step size or time-out is reached.