In this paper we describe and test a pipeline for the extraction and semantic labelling of geometrically salient points on acquired human body models. Points of interest are extracted on the preprocessed scanned geometries as maxima of the autodiffusion function at different scales and annotated by an expert, where possible, with a corresponding semantic label related to a specific anatomical location. On the extracted points we computed several descriptors (e.g. Heat Kernel Signature, Wave Kernel Signature, Derivatives of Heat Kernel Signature) and used labels and descriptors to train supervised classifiers, in order to understand if it is possible to recognize the points on new models. Experimental results show that this approach can be used to detect and recognize robustly at least a selection of landmarks on subjects with different body types and independently on pose and could therefore applied for automatic anthropometric analysis. 
Automatic labelling of anatomical landmarks on 3D body scans