Yongwei Miao

Short Communication to SMI 2011: Visual saliency guided normal enhancement technique for 3D shape depiction

Visual saliency can effectively guide the viewers visual attention to salient regions of a 3D shape. Incorporating the visual saliency measure of a polygonal mesh into the normal enhancement operation, a novel saliency guided shading scheme for shape depiction is developed in this paper. Due to the visual saliency measure of the 3D shape, our approach will adjust the illumination and shading to enhance the geometric salient features of the underlying model by dynamically perturbing the surface normals. The experimental results demonstrate that our non-photorealistic shading scheme can enhance the depiction of the underlying shape and the visual perception of its salient features for expressive rendering. Compared with previous normal enhancement techniques, our approach can effectively convey surface details to improve shape depiction without impairing the desired appearance.

Perceptual-saliency extremum lines for 3D shape illustration

Owing to their efficiency for conveying perceptual information of the underlying shape and their pleasing perceiving in visual aesthetics experience, line drawings are now becoming a widely used technique for illustrating 3D shapes. Using a center-surrounding bilateral filter operator on Gaussian-weighted average of local projection height between mesh vertices and their neighbors, a new perceptual-saliency measure which can depict surface salient features, is proposed in this paper. Due to the definition of perceptual-saliency measure, our perceptual-saliency extremum lines can be considered as the ridge-valley lines of perceptual-saliency measure along the principal curvature directions on triangular meshes. The experimental results demonstrate that these extremum lines effectively capture and depict 3D shape information visually, especially for archaeological artifacts.

Curvature-aware adaptive re-sampling for point-sampled geometry

With the emergence of large-scale point-sampled geometry acquired by high-resolution 3D scanning devices, it has become increasingly important to develop efficient algorithms for processing such models which have abundant geometric details and complex topology in general. As a preprocessing step, surface simplification is important and necessary for the subsequent operations and geometric processing. Owing to adaptive mean-shift clustering scheme, a curvature-aware adaptive re-sampling method is proposed for point-sampled geometry simplification. The generated sampling points are non-uniformly distributed and can account for the local geometric feature in a curvature aware manner, i.e. in the simplified model the sampling points are dense in the high curvature regions, and sparse in the low curvature regions. The proposed method has been implemented and demonstrated by several examples.

Differentials-based segmentation and parameterization for point-sampled surfaces

Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentials-based segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria: variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering (k-MC) algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method — multidimensional scaling (MDS) approach, and the parameterization results of all charts form an atlas for compact storage.

A Multi-Channel Salience Based Detail Exaggeration Technique for 3D Relief Surfaces

Visual saliency can always persuade the viewers visual attention to fine-scale mesostructure of 3D complex shapes. Owing to the multi-channel salience measure and salience-domain shape modeling technique, a novel visual saliency based shape depiction scheme is presented to exaggerate salient geometric details of the underlying relief surface. Our multi-channel salience measure is calculated by combining three feature maps, i.e., the 0-order feature map of local height distribution, the 1-order feature map of normal difference, and the 2-order feature map of mean curvature variation. The original relief surface is firstly manipulated by a salience-domain enhancement function, and the detail exaggeration surface can then be obtained by adjusting the surface normals of the original surface as the corresponding final normals of the manipulated surface. The advantage of our detail exaggeration technique is that it can adaptively alter the shading of the original shape to reveal visually salient features whilst keeping the desired appearance unimpaired. The experimental results demonstrate that our non-photorealistic shading scheme can enhance the surface mesostructure effectively and thus improving the shape depiction of the relief surfaces.

Vectorization of line drawing image based on junction analysis

Converting a scanned or shot line drawing image into a vector graph can facilitate further editand reuse, making it a hot research topic in computer animation and image processing. Besides avoiding noiseinfluence, its main challenge is to preserve the topological structures of the original line drawings, such as linejunctions, in the procedure of obtaining a smooth vector graph from a rough line drawing. In this paper, wepropose a vectorization method of line drawings based on junction analysis, which retains the original structureunlike done by existing methods. We first combine central line tracking and contour tracking, which allowsus to detect the encounter of line junctions when tracing a single path. Then, a junction analysis approachbased on intensity polar mapping is proposed to compute the number and orientations of junction branches.Finally, we make use of bending degrees of contour paths to compute the smoothness between adjacent branches,which allows us to obtain the topological structures corresponding to the respective ones in the input image.We also introduce a correction mechanism for line tracking based on a quadratic surface fitting, which avoidsaccumulating errors of traditional line tracking and improves the robustness for vectorizing rough line drawings.We demonstrate the validity of our method through comparisons with existing methods, and a large amount ofexperiments on both professional and amateurish line drawing images.创新点本文提出一种基于交叉点分析的线条矢量化方法, 克服了现有方法难以保持拓扑结构的不足。通过中心路径跟踪和轮廓路径跟踪相结合的方式, 准确检测交叉点的出现提出一种基于极坐标亮度映射的交叉点分析方法, 计算交叉点的分支数量和朝向; 利用轮廓路径的弯曲角度判断交叉点相邻分支间的光顺度, 从而获得与原图一致的拓扑结构。

SymmSketch: Creating symmetric 3D free-form shapes from 2D sketches

This paper presents SymmSketch—a system for creating symmetric 3D free-form shapes from 2D sketches. The reconstruction task usually separates a 3D symmetric shape into two types of shape components, that is, the self-symmetric shape component and the mutual-symmetric shape components. Each type can be created in an intuitive manner. Using a uniform symmetry plane, the user first draws 2D sketch lines for each shape component on a sketching plane. The z-depth information of the hand-drawn input sketches can be calculated using their property of mirror symmetry to generate 3D construction curves. In order to provide more freedom for controlling the local geometric features of the reconstructed free-form shapes (e.g., non-circular cross-sections), our modeling system creates each shape component from four construction curves. Using one pair of symmetric curves and one pair of general curves, an improved cross-sectional surface blending scheme is applied to generate a parametric surface for each component. The final symmetric free-form shape is progressively created, and is represented by 3D triangular mesh. Experimental results illustrate that our system can generate complex symmetric free-form shapes effectively and conveniently.

Feature sensitive re-sampling of point set surfaces with Gaussian spheres

Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification. Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner. The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme. A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme. Moreover, a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.

High frequency geometric detail manipulation and editing for point-sampled surfaces

In this paper, based on the new definition of high frequency geometric detail for point-sampled surfaces, a new approach for detail manipulation and a detail-preserving editing framework are proposed. Geometric detail scaling and enhancement can always produce fantastic effects by directly manipulating the geometric details of the underlying geometry. Detail-preserving editing is capable of preserving geometric details during the shape deformation of point-sampled model. For efficient editing, the point set of the model is first clustered by a mean shift scheme, according to its anisotropic geometric features and each cluster is abstracted as a simplification sample point (SSP). Our editing operation is implemented by manipulating the SSP first and then diffusing the deformation to all sample points on the underlying geometry. As a postprocessing step, a new up-sampling and relaxation procedure is proposed to refine the deformed model. The effectiveness of the proposed method is demonstrated by several examples.

A shape enhancement technique based on multi-channel salience measure

In this paper, we develop a novel visual saliency based shape enhancement technique for relief surfaces. It consists of three steps. Firstly, we calculate the multi-channel salience map of the underlying shape by combining three feature maps, i.e., the feature map of local height distribution, normal difference, and mean curvature variation. Secondly, we manipulate the original relief surface by a salience-domain shape manipulation function. Finally, we adjust surface normals of the original shape as the corresponding final normals of the manipulated surface. The experimental results show that our proposed algorithm can adjust the shading of the original shape and thus for improving its shape depiction.