Isosurface Reconstruction of Sharp Features and Geometry from Industrial CT Data

Computing Reliable Gradients from Scalar Data.

Sharp surface edges and corners often provide important visual information about the surface. However, algorithms for isosurface visualization often smooth sharp edges

and corners, hiding instead of highlighting the sharp features. Previous algorithms for identifying sharp isosurface features required gradients or surface normals to be provided

as input to the algorithm. We present an algorithm for determining sharp isosurface edges and corners from scalar data on a regular grid.

Our algorithm uses the central difference formula to construct gradient approximations in the scalar field, and then identifies which gradient approximations are reliable.

Because our algorithm uses only local information to determine sharp features, it is fast and easily parallelizable. Our algorithm can be used to generate

an isosurface with sharp features or to visualize or highlight the sharp features. We describe an algorithm ReliableGrad to construct reliable gradients

from scalar data with sharp features. We combine ReliableGrad with our previously published algorithm MergeSharp to construct

isosuraces with sharp edges and corners from industrial CT data. The resulting isosurface meshes have sharp edges and corners

reliably represented by mesh edges and vertices.

Journal Submission

SHREC: SHarp REConstruction (Submitted to Journal of Geometric Modelling.)

Technical Report