Estimating the principle curvatures and the Darboux frame from real 3D range data

Local differential properties of surfaces such as principle curvatures and the local Darboux frame are natural tools to be used during processes of object recognition or any other process, which involves geometric property extraction from 3D range data. As second-order derivative computations are involved in principle curvatures and principle directions computations, their estimation are highly sensitive to noise. The work presented here, makes some subtle but very important modifications to algorithms originally suggested by Taubin (1995), and Chen and Schmidt (1992) yielding more accurate estimations for those properties when real 3D data is involved