Abstract:
This paper presents a technique for creating a smooth, closed surface from
a set of 2D contours, which have been extracted from a 3D scan. The technique
interprets the pixels that make up the contours as points in R3 and employs
Multi-level Partition of Unity (MPU) implicit models to create a surface
that approximately fits to the 3D points. Since MPU implicit models
additionally require surface normal information at each point, an algorithm
that estimates normals from the contour data is also described. Contour
data frequently contains noise from the scanning and delineation process.
MPU implicit models provide a superior approach to the problem of
contour-based surface reconstruction, especially in
the presence of noise, because they are based on adaptive implicit
functions that locally approximate the points within a controllable
error bound. We demonstrate the effectiveness
of our technique with a number of example datasets, providing images and
error statistics generated from our results.