We present a novel approach to parameterize a mesh with disk topology to the plane in a shapeWe present also a more general "hybrid" parameterization model which provides a continuous spectrum of possibilities, controlled by a single parameter. The two cases described above lie at the two ends of the spectrum. We generalize our local/global algorithm to compute these parameterizations. The local phase may also be accelerated by parallelizing the independent computations per triangle.

The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is evaluated using a guidance signal instead of the original signal. It has been successfully applied to various image processing problems, where it provides more flexibility than the standard bilateral filter to achieve high quality results. On the other hand, its success is heavily dependent on the guidance signal, which should ideally provide a robust estimation about the features of the output signal. Such a guidance signal is not always easy to construct. In this paper, we propose a novel mesh normal filtering framework based on the joint bilateral filter, with applications in mesh denoising. Our framework is designed as a two-stage process: first, we apply joint bilateral filtering to the face normals, using a properly constructed normal field as the guidance; afterwards, the vertex positions are updated according to the filtered face normals. We compute the guidance normal on a face using a neighboring patch with the most consistent normal orientations, which provides a reliable estimation of the true normal even with a high-level of noise. The effectiveness of our approach is validated by extensive experimental results.The definitive version is available at

Figure 1: Given an input Horse model (a), our method generates a skin-frame structure (b), which is approximate to the model, to minimize the cost of material used in printing it. The frame structure is designed to meet various constraints by an optimization scheme. In (b) we remove the front part of the skin in order to show the internal structure of frame. (c) is the photo of an printed model by removing part of its skin to see the internal struts. (d) is the photo of the printed model generated by our method. A small red drawing pin is put under the object as a size reference in (c) and (d) respectively. The material usage in (d) is only 15.0% of that of a solid object. Abstract3D printers have become popular in recent years and enable fabrication of custom objects for home users. However, the cost of the material used in printing remains high. In this paper, we present an automatic solution to design a skin-frame structure for the purpose of reducing the material cost in printing a given 3D object. The frame structure is designed by an optimization scheme which significantly reduces material volume and is guaranteed to be physically stable, geometrically approximate, and printable. Furthermore, the number of struts is minimized by solving an 0 sparsity optimization. We formulate it as a multi-objective programming problem and an iterative extension of the preemptive algorithm is developed to find a compromise solution. We demonstrate the applicability and practicability of our solution by printing various objects using both powder-type and extrusion-type 3D printers. Our method is shown to be more cost-effective than previous works.

0 RT DA VB SZ 1 0.2 0.4 0.6 0.8 Sum (a) (b) (c) Figure 1: Optimizing the aesthetics of the original photograph in (a) by our approach leads to the new image composition shown in (c). (b) shows the cropping result of the approach of [Santella et al. 2006]. The aesthetic scores are shown in (d). Our result in (c) obtains higher aesthetic score than (a). RT(rule of thirds), DA(diagonal), VB(visual balance), and SZ(region size) are components of the objective function. Abstract Aesthetic images evoke an emotional response that transcends mere visual appreciation. In this work we develop a novel computational means for evaluating the composition aesthetics of a given image based on measuring several well-grounded composition guidelines. A compound operator of crop-and-retarget is employed to change the relative position of salient regions in the image and thus to modify the composition aesthetics of the image. We propose an optimization method for automatically producing a maximally-aesthetic version of the input image. We validate the performance of the method and show its effectiveness in a variety of experiments.

We present Easy Mesh Cutting, an intuitive and easy-to-use mesh cutout tool. Users can cut meaningful components from meshes by simply drawing freehand sketches on the mesh. Our system provides instant visual feedback to obtain the cutting results based on an improved region growing algorithm using a feature sensitive metric. The cutting boundary can be automatically optimized or easily edited by users. Extensive experimentation shows that our approach produces good cutting results while requiring little skill or effort from the user and provides a good user experience. Based on the easy mesh cutting framework, we introduce two applications including sketch-based mesh editing and mesh merging for geometry processing.

The bidirectional texture function (BTF) is a 6D function that can describe textures arising from both spatially-variant surface reflectance and surface mesostructures. In this paper, we present an algorithm for synthesizing the BTF on an arbitrary surface from a sample BTF. A main challenge in surface BTF synthesis is the requirement of a consistent mesostructure on the surface, and to achieve that we must handle the large amount of data in a BTF sample. Our algorithm performs BTF synthesis based on surface textons, which extract essential information from the sample BTF to facilitate the synthesis. We also describe a general search strategy, called the -coherent search, for fast BTF synthesis using surface textons. A BTF synthesized using our algorithm not only looks similar to the BTF sample in all viewing/lighthing conditions but also exhibits a consistent mesostructure when viewing and lighting directions change. Moreover, the synthesized BTF fits the target surface naturally and seamlessly. We demonstrate the effectiveness of our algorithm with sample BTFs from various sources, including those measured from real-world textures.

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Figure 1: The co-segmentation result of the Candelabra set by our algorithm. Starting from the over-segmented patches of the shapes (left), our algorithm automatically obtains the consistent segmentations among these objects by grouping the patches using subspace clustering in multiple feature spaces. Corresponding parts are shown in the same colors (right). AbstractWe present a novel algorithm for automatically co-segmenting a set of shapes from a common family into consistent parts. Starting from over-segmentations of shapes, our approach generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously. The core of the algorithm is to compute an affinity matrix where each entry encodes the similarity between two patches, which is measured based on the geometric features of patches. Instead of concatenating the different features into one feature descriptor, we formulate co-segmentation into a subspace clustering problem in multiple feature spaces. Specifically, to fuse multiple features, we propose a new formulation of optimization with a consistent penalty, which facilitates both the identification of most similar patches and selection of master features for two similar patches. Therefore the affinity matrices for various features are sparsity-consistent and the similarity between a pair of patches may be determined by part of (instead of all) features. Experimental results have shown how our algorithm jointly extracts consistent parts across the collection in a good manner.