Multi-view Wire Art

Kai-Wen Hsiao
National Tsing Hua University

Jia-Bin Huang
Virginia Tech

Hung-Kuo Chu
National Tsing Hua University

ACM Transactions on Graphics (Proc. of SIGGRAPH Asia 2018)

Abstract

Wire art is the creation of three-dimensional sculptural art using wire strands. As the 2D projection of a 3D wire sculpture forms line drawing patterns, it is possible to craft multi-view wire sculpture art — a static sculpture with multiple (potentially very different) interpretations when perceived at different viewpoints. Artists can effectively leverage this characteristic and produce compelling artistic effects. However, the creation of such multiview wire sculpture is extremely time-consuming even by highly skilled artists. In this paper, we present a computational framework for automatic creation of multi-view 3D wire sculpture. Our system takes two or three user-specified line drawings and the associated viewpoints as inputs. We start with producing a sparse set of voxels via greedy selection approach such that their projections on the virtual cameras cover all the contour pixels of the input line drawings. The sparse set of voxels, however, do not necessarily form one single connected component. We introduce a constrained 3D pathfinding algorithm to link isolated groups of voxels into a connected component while maintaining the similarity between the projected voxels and the line drawings. Using the reconstructed visual hull, we extract a curve skeleton and produce a collection of smooth 3D curves by fitting cubic splines and optimizing the curve deformation to best approximate the provided line drawings. We demonstrate the effectiveness of our system for creating compelling multi-view wire sculptures in both simulation and 3D physical printouts.

Algorithm

Method overview. Given three input line drawing images (a), our system starts with reconstructing a discrete visual hull (b) throgh intersecting generalized cones formed by back-projecting the 2D image to 3D using the associated camera parameters (mutually orthogonal viewpoints in this example). We then integrate the isolated components (green voxels) into a connected visual hull (c) via a 3D pathfinding method that jointly analyzes the spatial relations between components in 3D and their 2D counterparts. The traced 3D paths are represented by blue voxels. Next, we apply a volumetric thinning algorithm [Liu et al. 2010] to extract a curve skeleton from the reconstructed visual hull, followed by a structure simplification process that grounds a tailormade quality measurement to strike a balance between i) the projection error and ii) the structure compactness of resulting curve skeleton (d). Lastly, we fit the individual skeleton lines (colored line segments) with cubic splines and employ an image-guided 3D curve deformation to obtain the final smooth, continuous, and compact 3D wire sculpture (e). (Top row) The projections of intermediate products using associated camera parameters.

Results

We present several examples of input line drawings with varying complexity. Given a set of three input images (top), our system automatically generates 3D wires that exhibit three distinct line drawings when perceived from three orthogonal view points (bottom). Our method handles inputs with varying complexity. More results can be found in the Online Gallery.

Multi-view Wire Art in the press:

Seamless Virtual Reality News

Acknowledgement

We are grateful to the anonymous reviewers for their comments and suggestions. We also like to thank National Applied Research Laboratories and Xian-Guang Zhong for helping on the 3D printing. The work is supported in part by the Ministry of Science and Technology of Taiwan (107-2218-E-007-047- and 107-2221-E-007-088-MY3).

Bibtex

@article{Hsiao:2018:MVWA:,
 author = {Hsiao, Kai-Wen and Huang, Jia-Bin and Chu, Hung-Kuo},
 title = {Multi-view Wire Art},
 journal = {ACM Trans. Graph.},
 volume = {37},
 number = {6},
 year = {2018},
 pages = {242:1--242:11},
 articleno = {242},
 numpages = {11}
}

Links

Paper (small) (4.53MB)

Paper (186.18MB)

Slides (76.49MB)

Supplementary (169.77MB)

Online Gallery

Github