An adaptative filtering method to evalute normal vectors and surface areas of 3d objects. Application to snow images from X-ray tomography
Résumé
Estimating the normal vector field on the boundary of discrete 3D objects is essential for rendering and image measurement problems. Most of the existing algorithms do not provide an accurate determination of the normal vector field for shapes that present edges. We propose here a new and simple computational method in order to obtain accurate results on all types of shapes whatever their local convexity degree is. The presented method is based on the gradient vector field analysis of the object distance map. This vector field is adaptively filtered around each surface voxel using angle and symmetry criteria, so that as many relevant contributions as possible are accounted for. This optimizes the smoothing of digitization effects while preserving relevant details of the processed numerical object. Thanks to the precise normal field obtained, a projection method can be proposed to derive immediately the surface area from a raw discrete object. An empirical justification of the validity of such an algorithm in the continuous limit is also provided. Some results on simulated data and snow images from X-ray tomography are presented, compared to the Marching Cubes and Convex Hull results and discussed.
Domaines
Traitement des images [eess.IV]| Origine | Fichiers éditeurs autorisés sur une archive ouverte |
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