View-Independent Wire Art Modeling via Manifold Fitting

Abstract

This paper presents a novel fully automated method for generating view-independent abstract wire art from 3D models. The main challenge in creating line art is to strike a balance among abstraction, structural clarity, 3D perception, and consistent aesthetics from different viewpoints. Many existing approaches have been proposed, including extracting wire art from mesh, reconstructing it from pictures, etc. But they all suffer from the fact that the wires are usually very unorganized and cumbersome and usually can only guarantee the observation effect of specific viewpoints. To overcome these problems, we propose a paradigm shift: instead of predicting the line segments directly, we consider the generation of wire art as an optimization-driven manifold-fitting problem. Thus we can abstract/generalize the 3D model while retaining the key properties necessary for appealing line art, including structural topology and connectivity, and maintain the three-dimensionality of the line art with a multi-perspective view. Experimental results show that our view-independent method outperforms previous methods in terms of line simplicity, shape fidelity, and visual consistency.