Understanding 3D scenes from range images need the segmentation of 3D
surfaces into approximate planar surface patches from which curved sur
faces can be constructed quickly for high level vision purpose [Biswas
el al., Qualitative description of three-dimensional scenes, Intl. J.
Pattern Recognition and Artificial Intelligence 6(4), 651-672 (1992)]
. In this paper we have presented a new parallel algorithm for 3D surf
ace segmentation wherein the problem of surface segmentation is modell
ed as a quantization problem. The surface normals are quantized to som
e predefined directions, or stated otherwise, the surface regions are
approximated by planar surface patches which are parallel to some pred
efined planes. The novelty of the algorithm lies in the fact that, tho
ugh surface segmentation is achieved by quantization of surface normal
s, the algorithm does not compute the surface normals explicitly. Rath
er the quantization is achieved using simple operations of shift, subt
ract and threshold. This technique suitably avoids the computations of
the differential properties of the surfaces or the surface fitting ex
pressions which are used in most of the other existing techniques. Hen
ce this approach is computationally attractive. This algorithm is easi
ly implementable on an SIMD array computer. Another advantage of this
technique is that it is robust to noise present in the image. The algo
rithm has been explained with a number of examples. Experimental resul
ts with synthetic as well as real range images are cited in this paper
to highlight distinctive features of the algorithm.