Hierarchical Deformation of Locally Rigid Meshes

We propose a method for calculating deformations of models by deforming a low‐resolution mesh and adding details while ensuring that the details we add satisfy a set of constraints. Our method builds a low‐resolution representation of a mesh by using edge collapses and performs an as‐rigid‐as‐possible deformation on the simplified mesh. We then add back details by reversing edge‐collapses so that the shape of the mesh is locally preserved. While adding details, we deform the mesh to match the predicted positions of constraints so that constraints on the full‐resolution mesh are met. Our method operates on meshes with arbitrary triangulations, satisfies constraints over the full‐resolution mesh and converges quickly.     

A curvature estimation for pen input segmentation in sketch-based modeling

A proper segmentation of pen marking enhances shape recognition and enables a natural interface for sketch-based modeling from simple line drawing tools to 3D solid modeling applications; user input is otherwise restricted to draw only one segment per one stroke. In general, the pen marking segmentation is achieved by detecting the points of high curvature-called, segmenting points-and splitting the pen marking at those points. This paper presents a curvature estimation method, which considers only local shape information. The proposed method can therefore estimate curvature on-the-fly while user is drawing on a pen-input display, such as tablet PCs.     

State‐of‐the‐Art Report on Temporal Coherence for Stylized Animations

Non‐photorealistic rendering (NPR) algorithms allow the creation of images in a variety of styles, ranging from line drawing and pen‐and‐ink to oil painting and watercolour. These algorithms provide greater flexibility, control and automation over traditional drawing and painting. Despite significant progress over the past 15 years, the application of NPR to the generation of stylized animations remains an active area of research. The main challenge of computer‐generated stylized animations is to reproduce the look of traditional drawings and paintings while minimizing distracting flickering and sliding artefacts present in hand‐drawn animations. These goals are inherently conflicting and any attempt to address the temporal coherence of stylized animations is a trade‐off. This state‐of‐the‐art report is motivated by the growing number of methods proposed in recent years and the need for a comprehensive analysis of the trade‐offs they propose. We formalize the problem of temporal coherence in terms of goals and compare existing methods accordingly. We propose an analysis for both line and region stylization methods and discuss initial steps towards their perceptual evaluation. The goal of our report is to help uninformed readers to choose the method that best suits their needs, as well as motivate further research to address the limitations of existing methods.     

Consistent Shape Matching via Coupled Optimization

We propose a new method for computing accurate point‐to‐point mappings between a pair of triangle meshes given imperfect initial correspondences. Unlike the majority of existing techniques, we optimize for a map while leveraging information from the inverse map, yielding results which are highly consistent with respect to composition of mappings. Remarkably, our method considers only a linear number of candidate points on the target shape, allowing us to work directly with high resolution meshes, and to avoid a delicate and possibly error‐prone up‐sampling procedure. Key to this dimensionality reduction is a novel candidate selection process, where the mapped points drift over the target shape, finalizing their location based on intrinsic distortion measures. Overall, we arrive at an iterative scheme where at each step we optimize for the map and its inverse by solving two relaxed Quadratic Assignment Problems using off‐the‐shelf optimization tools. We provide quantitative and qualitative comparison of our method with several existing techniques, and show that it provides a powerful matching tool when accurate and consistent correspondences are required.     

基于形状演化的线条画风格转换与变形

根据线条画和线条画中笔画的形状特征,运用平面形状演化理论对线条画和笔画的形状进行平滑或增强,并对宽度进行相应的调整,最后得到不同风格的线条画．该风格转换水需要任何样本,而且能够控制转换的程度,提高了线条画风格转换的自由度．     

Elastic Correspondence between Triangle Meshes

We propose a novel approach for shape matching between triangular meshes that, in contrast to existing methods, can match crease features. Our approach is based on a hybrid optimization scheme, that solves simultaneously for an elastic deformation of the source and its projection on the target. The elastic energy we minimize is invariant to rigid body motions, and its non‐linear membrane energy component favors locally injective maps. Symmetrizing this model enables feature aligned correspondences even for non‐isometric meshes. We demonstrate the advantage of our approach over state of the art methods on isometric and non‐isometric datasets, where we improve the geodesic distance from the ground truth, the conformal and area distortions, and the mismatch of the mean curvature functions. Finally, we show that our computed maps are applicable for surface interpolation, consistent cross‐field computation, and consistent quadrangular remeshing of a set of shapes.     

Beyond Stippling — Methods for Distributing Objects on the Plane

Conventionally, stippling is an effective technique for representing surfaces in pen‐and‐ink. We present new efficientmethods for stipple drawings by computer. In contrast to already existing techniques, arbitrary shapes canbe used in place of dots. An extension of Lloyd's Method enables us to position small objects on a plane in a visuallypleasing form. This allows us to generate new illustration styles. Similar methods can be used for positioningobjects in other applications.     

DTI in Context: Illustrating Brain Fiber Tracts In Situ

We present an interactive illustrative visualization method inspired by traditional pen‐and‐ink illustration styles. Specifically, we explore how to provide context around DTI fiber tracts in the form of surfaces of the brain, the skull, or other objects such as tumors. These contextual surfaces are derived from either segmentation data or generated using interactive iso‐surface extraction and are rendered with a flexible, slice‐based hatching technique, controlled with ambient occlusion. This technique allows us to produce a consistent and frame‐coherent appearance with precise control over the lines. In addition, we provide context through cutting planes onto which we render gray matter with stippling. Together, our methods not only facilitate the interactive exploration and illustration of brain fibers within their anatomical context but also allow us to produce high‐quality images for print reproduction. We provide evidence for the success of our approach with an informal evaluation with domain experts.     

Multiscale Biharmonic Kernels

This paper introduces a general principle for constructing multiscale kernels on surface meshes, and presents a construction of the multiscale pre‐biharmonic and multiscale biharmonic kernels. Our construction is based on an optimization problem that seeks to minimize a smoothness criterion, the Laplacian energy, subject to a sparsity inducing constraint. Namely, we use the lasso constraint, which sets an upper bound on the l₁ ‐norm of the solution, to obtain a family of solutions parametrized by this upper‐bound parameter. The interplay between sparsity and smoothness results in smooth kernels that vanish away from the diagonal. We prove that the resulting kernels have gradually changing supports, consistent behavior over partial and complete meshes, and interesting limiting behaviors (e.g. in the limit of large scales, the multiscale biharmonic kernel converges to the Green's function of the biharmonic equation); in addition, these kernels are based on intrinsic quantities and so are insensitive to isometric deformations. We show empirically that our kernels are shape‐aware, are robust to noise, tessellation, and partial object, and are fast to compute. Finally, we demonstrate that the new kernels can be useful for function interpolation and shape correspondence.     

A Stylized Approach for Pencil Drawing from Photographs

We present a stylized scheme that produces pencil drawings in a range of styles from an image. To produce controllable pencil drawing effects and remedy the problems of existing convolution‐based schemes, we develop a swing bilateral LIC (SBL) filter. Our first approach to express the styled pencil drawings is to control the directions of pencil strokes that depicts both shapes and smooth tone. Another approach is to produce colors of pencil drawings by sampling colors from real color pencils. The third approach is to mimic the artistic technique that increases the details of drawings in a progressive manner. We present drawings in several styles and compare some of them directly with illustrations taken from an artists' work.     

Implicit Hierarchical Quad‐Dominant Meshes

We present a method for producing quad‐dominant subdivided meshes, which supports both adaptive refinement and adaptive coarsening. A hierarchical structure is stored implicitly in a standard half‐edge data structure, while allowing us to efficiently navigate through the different level of subdivision. Subdivided meshes contain a majority of quad elements and a moderate amount of triangles and pentagons in the regions of transition across different levels of detail. Topological LOD editing is controlled with local conforming operators, which support both mesh refinement and mesh coarsening. We show two possible applications of this method: we define an adaptive subdivision surface scheme that is topologically and geometrically consistent with the Catmull–Clark subdivision; and we present a remeshing method that produces semi‐regular adaptive meshes.     

Computing discrete shape operators on general meshes

Discrete curvature and shape operators, which capture complete information about directional curvatures at a point, are essential in a variety of applications: simulation of deformable two‐dimensional objects, variational modeling and geometric data processing. In many of these applications, objects are represented by meshes. Currently, a spectrum of approaches for formulating curvature operators for meshes exists, ranging from highly accurate but computationally expensive methods used in engineering applications to efficient but less accurate techniques popular in simulation for computer graphics. We propose a simple and efficient formulation for the shape operator for variational problems on general meshes, using degrees of freedom associated with normals. On the one hand, it is similar in its simplicity to some of the discrete curvature operators commonly used in graphics; on the other hand, it passes a number of important convergence tests and produces consistent results for different types of meshes and mesh refinement.     

Shape-aware Volume Illustration

We introduce a novel volume illustration technique for regularly sampled volume datasets. The fundamental difference between previous volume illustration algorithms and ours is that our results are shape-aware, as they depend not only on the rendering styles, but also the shape styles. We propose a new data structure that is derived from the input volume and consists of a distance volume and a segmentation volume. The distance volume is used to reconstruct a continuous field around the object boundary, facilitating smooth illustrations of boundaries and silhouettes. The segmentation volume allows us to abstract or remove distracting details and noise, and apply different rendering styles to different objects and components. We also demonstrate how to modify the shape of illustrated objects using a new 2D curve analogy technique. This provides an interactive method for learning shape variations from 2D hand-painted illustrations by drawing several lines. Our experiments on several volume datasets demonstrate that the proposed approach can achieve visually appealing and shape-aware illustrations. The feedback from medical illustrators is quite encouraging.     

Online Personalised Non‐photorealistic Rendering Technique for 3D Geometry from Incremental Sketching

This paper presents an online personalised non‐photorealistic rendering (NPR) technique for 3D models generated from interactively sketched input. This technique has been integrated into a sketch‐based modelling system. It lets users interact with computers by drawing naturally, without specifying the number, order, or direction of strokes. After sketches are interpreted as 3D objects, they can be rendered with personalised drawing styles so that the reconstructed 3D model can be presented in a sketchy style similar in appearance to what have been drawn for the 3D model. This technique captures the user's drawing style without using template or prior knowledge of the sketching style. The personalised rendering style can be applied to both visible and initially invisible geometry. The rendering strokes are intelligently selected from the input sketches and mapped to edges of the 3D object. In addition, non‐geometric information such as surface textures can be added to the recognised object in different sketching modes. This will integrate sketch‐based incremental 3D modelling and NPR into conceptual design.     

Accurate Simplification of Multi‐Chart Textured Models

Scanning and acquisition methods produce highly detailed surface meshes that need multi‐chart parameterizations to reduce stretching and distortion. From these complex shape surfaces, high‐quality approximations are automatically generated by using surface simplification techniques. Multi‐chart textures hinder the quality of the simplification of these techniques for two reasons: either the chart boundaries cannot be simplified leading to a lack of geometric fidelity; or texture distortions and artefacts appear near the simplified boundaries. In this paper, we present an edge‐collapse based simplification method that provides an accurate, low‐resolution approximation from a multi‐chart textured model. For each collapse, the model is reparameterized by local bijective mappings to avoid texture distortions and chart boundary artefacts on the simplified mesh due to the geometry changes. To better apply the appearance attributes and to guarantee geometric fidelity, we drive the simplification process with the quadric error metrics weighted by a local area distortion measure.     

Out‐of‐Core Simplification and Crack‐Free LOD Volume Rendering for Irregular Grids

We propose a novel out‐of‐core simplification and level‐of‐detail (LOD) volume rendering algorithm for large irregular grids represented as tetrahedral meshes. One important feature of our algorithm is that it creates a space decomposition as required by I/O‐efficient simplification and volume rendering, and simplifies both the internal and boundary portions of the sub‐volumes progressively by edge collapses using the (extended) quadric error metric, while ensuring any selected LOD mesh to be crack‐free (i.e., any neighboring sub‐volumes in the LOD have consistent boundaries, and all the cells in the LOD do not have negative volumes), with all computations performed I/O‐ejficiently. This has been an elusive goal for out‐of‐core progressive meshes and LOD visualization, and our novel solution achieves this goal with a theoretical guarantee to be crack‐free for tetrahedral meshes. As for selecting a desirable LOD mesh for volume rendering, our technique supports selective refinement LODs (where different places can have different error bounds), in addition to the basic uniform LODs (where the error bound is the same in all places). The proposed scalar‐value range and view‐dependent selection queries for selective refinement are especially effective in producing images of the highest quality with a much faster rendering speed. The experiments demonstrate the efficacy of our new technique.     

Iconic and multi-stroke gesture recognition

Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.     

Field‐Aligned and Lattice‐Guided Tetrahedral Meshing

We present a particle‐based approach to generate field‐aligned tetrahedral meshes, guided by cubic lattices, including BCC and FCC lattices. Given a volumetric domain with an input frame field and a user‐specified edge length for the cubic lattice, we optimize a set of particles to form the desired lattice pattern. A Gaussian Hole Kernel associated with each particle is constructed. Minimizing the sum of kernels of all particles encourages the particles to form a desired layout, e.g., field‐aligned BCC and FCC. The resulting set of particles can be connected to yield a high quality field‐aligned tetrahedral mesh. As demonstrated by experiments and comparisons, the field‐aligned and lattice‐guided approach can produce higher quality isotropic and anisotropic tetrahedral meshes than state‐of‐the‐art meshing methods.