Building symbolic information for 3D human body modeling from range data

Abstract

The work is concerned with the signal-to-symbol problem of building skinned, segmented, landmarked and labeled 3D models of the whole human body from range data. A fully automated model based process is presented that takes raw range data, cleans and skins it, and then locates "interesting" features, to enrich the surface with symbolic information for specific applications. The method is validated via volumetrics in medicine and surface anthropometry.