@article{discovery1559261,
       publisher = {Association for Computing Machinery},
            year = {2017},
         journal = {ACM Transactions on Graphics (TOG)},
          number = {4},
            note = {{\copyright} 2017 ACM. This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           title = {Image-based Reconstruction of Wire Art},
          volume = {36},
           month = {July},
        keywords = {Computing methodologies {$\rightarrow$} 3D imaging; Reconstruction;
Parametric curve and surface models;},
          author = {Liu, L and Ceylan, D and Cheng, L and Wang, W and Mitra, N},
            issn = {0097-8930},
             url = {http://doi.org/10.1145/3072959.3073682},
        abstract = {Objects created by connecting and bending wires are common in furniture design, metal sculpting, wire jewelry, etc. Reconstructing such objects with traditional depth and image based methods is extremely difficult due to their unique characteristics such as lack of features, thin elements, and severe self-occlusions. We present a novel image-based method that reconstructs a set of continuous 3D wires used to create such an object, where each wire is composed of an ordered set of 3D curve segments. Our method exploits two main observations: simplicity - wire objects are often created using only a small number of wires, and smoothness - each wire is primarily smoothly bent with sharp features appearing only at joints or isolated points. In light of these observations, we tackle the challenging image correspondence problem across featureless wires by first generating multiple candidate 3D curve segments and then solving a global selection problem that balances between image and smoothness cues to identify the correct 3D curves. Next, we recover a decomposition of such curves into a set of distinct and continuous wires by formulating a multiple traveling salesman problem, which finds smooth paths, i.e., wires, connecting the curves. We demonstrate our method on a wide set of real examples with varying complexity and present high-fidelity results using only 3 images for each object. We provide the source code and data for our work in the project website.}
}