Jb. Huang et Ch. Menq, Automatic data segmentation for geometric feature extraction from unorganized 3-D coordinate points, IEEE ROBOT, 17(3), 2001, pp. 268-279
In this paper, a systematic approach is proposed to automatically extract g
eometric surface features from a point cloud composed of a set of unorganiz
ed three-dimensional (3-D) coordinate points by data segmentation. The poin
t cloud is sampled from the boundary surface of a mechanical component with
arbitrary shape. The proposed approach is composed of three steps. In the
first step, a mesh surface domain is reconstructed to establish explicit to
pological relation among the discrete points. The topological adjacency is
further optimized to recover the second order object geometry. In the secon
d step, curvature-based border detection is applied on the irregular mesh t
o extract both sharp borders with tangent discontinuity and smooth borders
with curvature discontinuity. Finally, the mesh patches separated by the ex
tracted borders are grouped together in the third step. For objects with co
mplex shape, multilevel segmentation scheme is proposed for better results.
The capability of the proposed approach is demonstrated using various poin
t clouds having distinct characteristics. Integrated with state of art scan
ning devices, the developed segmentation scheme can support reverse enginee
ring of high precision mechanical components. It has potential applications
in a whole spectrum of engineering problems with a major impact on rapid d
esign and prototyping, shape analysis, and virtual reality.