Feature-aligned segmentation using correlation clustering

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.

Surface remeshing with robust user-guided segmentation

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.

MDISN:Learning multiscale deformed implicit fields from single images

We present a multiscale deformed implicit surface network(MDISN)to reconstruct 3D objects from single images by adapting the implicit surface of the target object from coarse to fine to the input image.The basic idea is to optimize the implicit surface according to the change of consecutive feature maps from the input image.And with multi-resolution feature maps,the implicit field is refined progressively,such that lower resolutions outline the main object components,and higher resolutions reveal fine-grained geometric details.To better explore the changes in feature maps,we devise a simple field deformation module that receives two consecutive feature maps to refine the implicit field with finer geometric details.Experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed method compared to state-of-the-art methods.