Geometric Property Estimation from 3D Range Data Points Aided by Local Quadric Surface Fitting

Geometric property estimation of the scattered point data is a key task in reverse engineering. A common approach is to estimate the local surface geometric properties, such as normal, vectors and curvatures, at each point by locally fitting a low order parametric polynomial surface to a neighborhood of its nearest points. However, existing parametric surface methods do not always achieve satisfactory estimates because of theoretical and practical reasons. Motivated by improving geometric properties estimation we propose a novel approach based on local quadric surface fitting. Experimental results are presented to verify the feasibility of the proposed method.