Feature Preserving Sketching of Volume Data

Abstract

In this paper, we present a novel method for extracting feature lines from volume data sets. This leads to a reduction of visual complexity and provides an abstraction of the original data to important structural features. We employ a new iteratively reweighted least-squares approach that allows us to detect sharp creases and to preserve important features such as corners or intersection of feature lines accurately. Traditional least-squares methods This is important for both visual quality as well as reliable further processing in feature detection algorithms. Our algorithm is efficient and easy to implement, and nevertheless effective and robust to noise. We show results for a number of different data sets.