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Figure 1: A curve network (misc2) representing a genus-7 mechanical part (left), cycles found by our algorithm (middle), and surface patches generated from the cycles (right). The curve network contains 410 curves. Our algorithm completed in half a second. AbstractGenerating surfaces from 3D curve networks has been a longstanding problem in computer graphics. Recent attention to this area has resurfaced as a result of new sketch based modeling systems. In this work we present a new algorithm for finding cycles that bound surface patches. Unlike prior art in this area, the output of our technique is unrestricted, generating both manifold and nonmanifold geometry with arbitrary genus. The novel insight behind our method is to formulate our problem as finding local mappings at the vertices and curves of our network, where each mapping describes how incident curves are grouped into cycles. This approach lends us the efficiency necessary to present our system in an interactive design modeler, whereby the user can adjust patch constraints and change the manifold properties of curves while the system automatically re-optimizes the solution.

Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods, however, only complete the partial shape with a single output, ignoring the ambiguity when reasoning the missing geometry. Hence, we pose a multi-modal shape completion problem, in which we seek to complete the partial shape with multiple outputs by learning a one-to-many mapping. We develop the first multimodal shape completion method that completes the partial shape via conditional generative modeling, without requiring paired training data. Our approach distills the ambiguity by conditioning the completion on a learned multimodal distribution of possible results. We extensively evaluate the approach on several datasets that contain varying forms of shape incompleteness, and compare among several baseline methods and variants of our methods qualitatively and quantitatively, demonstrating the merit of our method in completing partial shapes with both diversity and quality.

Surface remeshing is widely required in modeling, animation, simulation, and many other computer graphics applications. Improving the elements' quality is a challenging task in surface remeshing. Existing methods often fail to efficiently remove poor-quality elements especially in regions with sharp features. In this paper, we propose and use a robust segmentation method followed by remeshing the segmented mesh. Mesh segmentation is initiated using an existing Live-wire interaction approach and is further refined using local mesh operations. The refined segmented mesh is finally sent to the remeshing pipeline, in which each mesh segment is remeshed independently. An experimental study compares our mesh segmentation method as well as remeshing results with representative existing methods. We demonstrate that the proposed segmentation method is robust and suitable for remeshing.

We present a deformation-driven approach to topology-varying 3D shape correspondence. In this paradigm, the best correspondence between two shapes is the one that results in a minimal-energy, possibly topology-varying, deformation that transforms one shape to conform to the other while respecting the correspondence. Our deformation model, called GeoTopo transform, allows both geometric and topological operations such as part split, duplication, and merging, leading to fine-grained and piecewise continuous correspondence results. The key ingredient of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation, and simultaneously allows topology changes. This is accomplished by connecting shape parts using structural rods, which behave similarly to virtual springs but simultaneously allow the encoding of energies arising from geometric, structural, and topological shape variations. Driven by the combined deformation energy, an optimal shape correspondence is obtained via a pruned beam search. We demonstrate our deformationdriven correspondence scheme on extensive sets of man-made models with rich geometric and topological variation and compare the results to state-of-the-art approaches.

We present an algorithm for segmenting a mesh into patches whose boundaries are aligned with prominent ridge and valley lines of the shape. Our key insight is that this problem can be formulated as correlation clustering (CC), a graph partitioning problem originating from the data mining community. The formulation lends two unique advantages to our method over existing segmentation methods. First, since CC is non-parametric, our method has few parameters to tune. Second, as CC is governed by edge weights in the graph, our method offers users direct and local control over the segmentation result. Our technical contributions include the construction of the weighted graph on which CC is defined, a strategy for rapidly computing CC on this graph, and an interactive tool for editing the segmentation. Our experiments show that our method produces qualitatively better segmentations than existing methods on a wide range of inputs.

Genome-wide association studies have identified polymorphisms at chromosome 9q22.23 as a new thyroid cancer (TC) susceptibility locus in populations of European descent. Since then, the relationship between three common variations (rs965513, rs1867277, and rs71369530) of FOXE1 and TC has been reported in various ethnic groups; however, the results have been inconclusive. To derive a more precise estimation of the relationship as well as to quantify the between-study heterogeneity and potential bias, a meta-analysis including 120,258 individuals from 16 studies was performed. An overall random-effect per-allele odds ratio (OR) of 1.74 (95 % confidence interval (95 % CI), 1.62-1.86, P<10(-5)) and 1.62 (95 % CI, 1.50-1.76, P<10(-5)) was found for the rs965513 and rs1867277 polymorphisms, respectively. In addition, we also detected significant association of FOXE1 polyalanine tract (rs71369530) with TC risk (OR=2.01; 95 % CI, 1.66-2.44, P<10(-5)). Significant associations were also detected under dominant and recessive genetic models. In the subgroup analysis by ethnicity, significantly increased risks were found for the rs965513 polymorphism among Caucasians (OR=1.79; 95 % CI, 1.69-1.91, P<10(-5)) and Asians (OR=1.42; 95 % CI, 1.12-1.81, P=0.004). Ethnicity was identified as a potential source of between-study heterogeneity for rs965513. When stratified by sample size, study design, histological types of TC, and radiation exposure status, significantly increased risks were found for the rs965513 polymorphism. This meta-analysis demonstrated that the three common variations on FOXE1 is a risk factor associated with increased TC susceptibility, but these associations vary in different ethnic populations.