Fast and Robust Detection of Crest Lines on Meshes

Shin Yoshizawa


In this research, we develop a fast and robust method for detecting crest lines on surfaces approximated by dense triangle meshes. The crest lines, salient surface features defined via first- and second-order curvature derivatives, are widely used for shape matching and interrogation purposes. Their practical extraction is difficult because it requires good estimation of high-order surface derivatives. Our approach to the crest line detection is based on estimating the curvature tensor and curvature derivatives via local polynomial fitting. Although in general local fitting methods do not lead to a high-quality estimation of high-order surface derivatives to compare with global ones, a careful choice of a discrete scheme for estimating the surface normal and a fitting method allows us to get very close to the quality of results obtained with global fitting methods.

Another difficulty in practical detection of the crest lines follows from the fact that the crest lines are not defined in planar, spherical, conical, cylindrical and other surface patches where the focal set (caustic) degenerates. In practice, due to noise and mesh irregularities, a straightforward detection of the crest lines will result in producing many spurious crest lines in such surface regions. A new thresholding scheme is proposed in order to filter them out. The scheme exploits interesting relationships between curvature extrema, the so-called MVS functional of Moreton and Sequin, and Dupin cyclides, surfaces whose focal sets degenerate to curves. Since the crest lines are salient view- and scale-independent surface features which correspond well to human perception of surface creases, it is natural to use them for adaptive mesh simplification. We do it via incorporating salient crest lines and their regions of influence into a standard mesh decimation scheme.

This research is collaborated with Dr. A. G. Belyaev and Prof. Dr. H.-P. Seidel. The paper is published in Proc. ACM Symposium on Solid and Physical Modeling (SPM), pp. 227-232, June 13-15, 2005 at MIT, Cambridge, MA, USA.


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