Automatic extraction of significant features from 3D point clouds by ellipsoidal skeleton. Applications in vision and geometrical characterization

We present a robust method for automatically constructing an ellipsoidal skeleton (e-skeleton) from a set of 3D points coming from segmented NMR or TDM images. To ensure steadiness and accuracy, all points of the objects are taken into account, including the inner ones. This skelelon will be essentially useful for object characterization, for comparisons between various measurements and as a basis for deformable models. It also provides good initial guess for surface reconstruction algorithms. On output of the entire process, we obtain an analytical description of the chosen entity, semantically zoomable (local features only or reconstructed surfaces), with any level of detail (LOD) by discretization step control in voxel or polygon format. This capability allows us to handle objects at interactive frame rates once the e-skeleton is computed. Each e-skeleton is stored as a multiscale CSG implicit tree. Applications cover a wide range in computer graphics, from CAD to medical imaging.