Yunjin Lee

Mesh scissoring with minima rule and part salience

This paper presents an intelligent scissoring operator for meshes. Unlike common approaches that segment a mesh using clustering schemes, here we introduce a method that concentrates on the contours for cutting. Our approach is based on the minima rule and part salience theory from the cognitive theory. The minima rule states that human perception usually divides a surface into parts along the concave discontinuity of the tangent plane. The part salience theory provides factors which determine the salience of segments. Our method first extracts features to find candidate contours based on the minima rule. Subsequently, these open contours are prioritized to select the most salient one. Then, the selected open contour is automatically completed to form a loop around a specific part of the mesh. This loop is used as the initial position of a 3D geometric snake. Before applying a snake, we measure the part salience of the segments obtained by the completed contour. If conditions for the salience are not met, the contour is rejected. Otherwise, the snake moves by relaxation until it settles to define the final scissoring position. In this paper, we focus on a fully automatic scissoring scheme; nevertheless, we also report on semi-automatic user interfaces for intelligent scissoring which are easy to use and intuitive.

Line drawings via abstracted shading

We describe a GPU-based algorithm for rendering a 3D model as a line drawing, based on the insight that a line drawing can be understood as an abstraction of a shaded image. We thus render lines along tone boundaries or thin dark areas in the shaded image. We extend this notion to the dual: we render highlight lines along thin bright areas and tone boundaries. We combine the lines with toon shading to capture broad regions of tone. The resulting line drawings effectively convey both shape and material cues. The lines produced by the method can include silhouettes. creases, and ridges, along with a generalization of suggestive contours that responds to lighting as well as viewing changes. The method supports automatic level of abstraction, where the size of depicted shape features adjusts appropriately as the camera zooms in or out. Animated models can be rendered in real time because costly mesh curvature calculations are not needed.

Geometric Snakes for Triangular Meshes

Feature detection is important in various mesh processing techniques, such as mesh editing, mesh morphing, mesh compression, and mesh signal processing. In spite of much research in computer vision, automatic feature detection even for images still remains a difficult problem. To avoid this difficulty, semi‐automatic or interactive techniques for image feature detection have been investigated. In this paper, we propose a geometric snake as an interactive tool for feature detection on a 3D triangular mesh. A geometric snake is an extension of an image snake, which is an active contour model that slithers from its initial position specified by the user to a nearby feature while minimizing an energy functional. To constrain the movement of a geometric snake onto the surface of a mesh, we use the parameterization of the surrounding region of a geometric snake. Although the definition of a feature may vary among applications, we use the normal changes of faces to detect features on a mesh. Experimental results demonstrate that geometric snakes can successfully capture nearby features from user‐specified initial positions.

Feature sensitive mesh segmentation with mean shift

Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method, which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh clarification technique. For clustering of mesh normals, we adopt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.

Intelligent mesh scissoring using 3D snakes

Mesh partitioning and parts extraction have become key ingredients for many mesh manipulation applications both manual and automatic. In this paper, we present an intelligent scissoring operator for meshes which supports both automatic segmentation and manual cutting. Instead of segmenting the mesh by clustering, our approach concentrates on finding and defining the contours for cutting. This approach is based on the minima rule, which states that human perception usually divides a surface into parts along the contours of concave discontinuity of the tangent plane. The technique uses feature extraction to find such candidate feature contours. Subsequently, such a contour can be selected either automatically or manually, or the user may draw a 2D line to start the scissoring process. The given open contour is completed to form a loop around a specific part of the mesh, and this loop is used as the initial position of a 3D geometric snake. The snake moves by relaxation until it settles to define the final scissoring position. This process uses several fundamental geometric mesh attributes, such as curvature and centricity, and enables both automatic segmentation and an easy-to-use intelligent-scissoring operator.

Mesh parameterization with a virtual boundary

Parameterization of a 3D triangular mesh is a fundamental problem in various applications of meshes. The convex combination approach is widely used for parameterization because of its good properties, such as fast computation and one-to-one embedding. However, the approach has a drawback: most boundary triangles have high distortion in the embedding compared with interior ones. In this paper, we present an extension of the convex combination approach that resolves the drawback by using a virtual boundary. Since the virtual boundary is fixed onto a given convex polygon instead of the real boundary, the real boundary triangles can better reflect the shape of the corresponding 3D triangles. The proposedapproach obtains a parameterization of a 3D mesh with less d istortion than with the original convex combination approach. r 2002 Elsevier Science Ltd. All rights reserved.

Feature-guided image stippling

This paper presents an automatic method for producing stipple renderings from photographs, following the style of professional hedcut illustrations. For effective depiction of image features, we introduce a novel dot placement algorithm which adapts stipple dots to the local shapes. The core idea is to guide the dot placement along ‘feature flow’ extracted from the feature lines, resulting in a dot distribution that conforms to feature shapes. The sizes of dots are adaptively determined from the input image for proper tone representation. Experimental results show that such feature‐guided stippling leads to the production of stylistic and feature‐emphasizing dot illustrations.

Surface and normal ensembles for surface reconstruction

The majority of the existing techniques for surface reconstruction and the closely related problem of normal reconstruction are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. An ensemble is a statistical technique which can improve the performance of deterministic algorithms by putting them into a statistics based probabilistic setting. In this paper, we study the suitability of ensembles in normal and surface reconstruction. We experimented with a widely used normal reconstruction technique [Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. Surface reconstruction from unorganized points. Computer Graphics 1992;71-8] and Multi-level Partitions of Unity implicits for surface reconstruction [Ohtake Y, Belyaev A, Alexa M, Turk G, Seidel H-P. Multi-level partition of unity implicits. ACM Transactions on Graphics 2003;22(3):463-70], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.

Structure grid for directional stippling

This paper presents a novel method to convert a photograph into a stipple illustration. Our method addresses directional stippling, where the collective flows of dots are directed parallel and/or orthogonal to the local feature orientations. To facilitate regular and directional spacing of dots, we introduce the notion of a structure grid, which is extracted from the smoothed feature orientation field. We represent a structure grid as a 2D texture and develop an efficient construction algorithm that outperforms conventional Lloyd’s method in terms of the rigor of dot alignment. Moreover, the criss-crossing nature of a structure grid allows for the inclusion of line primitives, providing effective description of dark tone. Given a structure grid, we determine the appropriate positions and attributes of primitives in the final illustration via rapid pixel-based primitive rendering. Experimental results show that our directional stippling method nicely reproduces features and tones of various input images.

Neural mesh ensembles

This work proposes the use of neural network ensembles to boost the performance of a neural network based surface reconstruction algorithm. Ensemble is a very popular and powerful statistical technique based on the idea of averaging several outputs of a probabilistic algorithm. In the context of surface reconstruction, two main problems arise. The first is finding an efficient way to average meshes with different connectivity, and the second is tuning the parameters for surface reconstruction to maximize the performance of the ensemble. We solve the first problem by voxelizing all the meshes on the same regular grid and taking majority vote on each voxel. We tune the parameters experimentally, borrowing ideas from weak learning methods.