Figure 1: A complex model (left) consisting of 108 components is analyzed and 250 intelligent wires (in green) are extracted. Editing a few wires induces a new wire configuration (in blue) and leads to the result on the right. Man-made objects are largely dominated by a few typical features that carry special characteristics and engineered meanings. Stateof-the-art deformation tools fall short at preserving such characteristic features and global structure. We introduce iWIRES, a novel approach based on the argument that man-made models can be distilled using a few special 1D wires and their mutual relations. We hypothesize that maintaining the properties of such a small number of wires allows preserving the defining characteristics of the entire object. We introduce an analyze-and-edit approach, where prior to editing, we perform a light-weight analysis of the input shape to extract a descriptive set of wires. Analyzing the individual and mutual properties of the wires, and augmenting them with geometric attributes makes them intelligent and ready to be manipulated. Editing the object by modifying the intelligent wires leads to a powerful editing framework that retains the original design intent and object characteristics. We show numerous results of manipulation of man-made shapes using our editing technique.

Abstract—Sharp edges, ridges, valleys, and prongs are critical for the appearance and an accurate representation of a 3D model. In this paper, we propose a novel approach that deals with the global shape of features in a robust way. Based on a remeshing algorithm which delivers an isotropic mesh in a feature-sensitive metric, features are recognized on multiple scales via integral invariants of local neighborhoods. Morphological and smoothing operations are then used for feature region extraction and classification into basic types such as ridges, valleys, and prongs. The resulting representation of feature regions is further used for feature-specific editing operations.

We propose a fast and robust method for detecting crest lines on surfaces approximated by dense triangle meshes. The crest lines, salient surface features defined via first- and second-order curvature derivatives, are widely used for shape matching and interrogation purposes. Their practical extraction is difficult because it requires good estimation of high-order surface derivatives. Our approach to the crest line detection is based on estimating the curvature tensor and curvature derivatives via local polynomial fitting. Since the crest lines are not defined in the surface regions where the surface focal set (caustic) degenerates, we introduce a new thresholding scheme which exploits interesting relationships between curvature extrema, the so-called MVS functional of Moreton and Sequin, and Dupin cyclides, An application of the crest lines to adaptive mesh simplification is also considered.

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. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Geometric algorithms, languages, and systems 1.

3D mesh segmentation is a fundamental process for Digital Shape Reconstruction in a variety of applications including Reverse Engineering, Medical Imaging, etc. It is used to provide a high level representation of the raw 3D data which is required for CAD, CAM and CAE. In this paper, we present an exhaustive overview of 3D mesh segmentation methodologies examining their suitability for CAD models. In particular, a classification of the various methods is given based on their corresponding underlying fundamental methodology concept as well as on the distinct criteria and features used in the segmentation process.

Sculptural reliefs are widely used in various industries for purposes such as applying brands to packaging and decorating porcelain. In order to easily apply reliefs to CAD models, it is often desirable to reverse-engineer previously designed and manufactured reliefs. 3D scanners can generate triangle meshes from objects with reliefs; however, previous mesh segmentation work has not considered the particular problem of separation of reliefs from background. We consider here the specific case of segmenting a simple relief delimited by a single outer contour, which lies on a smooth, slowly varying background. Generally, such reliefs meet the surrounding surface in a small step, enabling us to devise a specific method for such relief segmentation. We find the boundary between the background and the relief using an adaptive snake. It starts at a simple user-drawn contour, and is driven inwards by a collapsing force until it matches the relief's boundary. Our method is insensitive to the choice of the initial contour. The snake's limiting position is controlled by a feature energy term designed to find a step. A refinement strategy is then used to drive the snake into concavities of the relief contour. We demonstrate operation of our algorithm using real scanned models with different relief contour shapes and triangle meshes with different resolutions.

Segmentation of geometric reliefs from a textured background has various applications in reverse engineering. We consider two approaches to solve this problem. The first classifies parts of a surface mesh as relief or background, and then uses a snake which moves inwards towards the desired relief boundary, which is coarsely located using an energy based on the classification. The second approach initially smoothes the surface to eliminate the background texture, and locates the snake at the relief boundary using an energy based on the step between the background and the relief. Both snakes start at simple user-drawn contours, and are driven towards the relief boundaries by the snake energy functional. In both cases, the snake has different evolution phases with different energy terms, to initially rapidly drive the snake towards the relief boundary, and to later accurately match it. To describe geometric textures, we analyze surface differential properties, and integral and statistical quantities based upon them, computed at multiple scales taken over local neighborhoods, following similar ideas from image texture processing. For classification, we use a support vector machine together with sequential forward floating search for feature selection. A straightforward Laplacian method is used for smoothing. We use example scanned models to demonstrate that both approaches are useful, but are suitable for different types of model.

metal stool scanned pointset skeletal snakes arterial snake network edited model Figure 1: Starting from a noisy raw scan with large parts missing our algorithm analyzes and extracts a curve network with associated cross-sectional profiles providing a reconstructed model. The extracted high-level shape representation enables easy, intuitive, yet powerful geometry editing. Note that our algorithm is targeted towards delicate 1D features and fails to detect the small disc at the top of the stool. Man-made objects often consist of detailed and interleaving structures, which are created using cane, coils, metal wires, rods, etc. The delicate structures, although manufactured using simple procedures, are challenging to scan and reconstruct. We observe that such structures are inherently 1D, and hence are naturally represented using an arrangement of generating curves. We refer to the resultant surfaces as arterial surfaces. In this paper we approach for analyzing, reconstructing, and manipulating such arterial surfaces. ∗ Corresponding authors:

In the last years, terrestrial laser scanning (TLS) instruments are gaining more and more importance for the 3D data acquisition for a variety of applications. However, the subsequent modelling procedures are still not standardised for the different applications and often the result of a TLS project is a registered 3D point cloud. This form of object representation is often applicable for visualisation purposes, but for the mathematical analysis of the object, advanced representation forms are essential. For this aim, the meshing of the TLS point cloud is a common procedure. However, it has to be considered that this form of object representation can be directly altered by random, systematic, and gross errors included in the TLS data. Especially discontinuities are usually erroneously due to the complex interaction of the laser beam with the illuminated object surface. Thus, methods that consider possible measurement errors within the process of 3D modelling are essential. Furthermore, an adequate representation of structure lines within the modelling procedure is important for a high quality object representation. For this aim, an explicit description of these linear features is necessary. Therefore, in order to enhance the models generated from a TLS point cloud, this paper focuses on the determination of structure lines. The method is based on a semi-automatic process originally developed for the modelling of breaklines from airborne laser scanner data. Within this paper, the adaptation of the modelling framework to the usage of TLS data is presented. Furthermore, it is extended to further types of structure lines. The modelling framework is based on the irregular distributed TLS point cloud. The usage of a robust estimation procedure together with a sensor specific stochastical model allows the consideration of measurement errors within the structure line modelling procedure. For the modelling concept, an

Abstract—This paper considers the problem of interactively finding the cutting contour to extract components from an existing mesh. First, we propose a constrained random walks algorithm that can add constraints to the random walks procedure and thus allows for a variety of intuitive user inputs. Second, we design an optimization process that uses the shortest graph path to derive a nice cut contour. Then a new mesh cutting algorithm is developed based on the constrained random walks plus the optimization process. Within the same computational framework, the new algorithm provides a novel user interface for interactive mesh cutting that supports three typical user inputs and also their combinations: 1) foreground/background seed inputs: the user draws strokes specifying seeds for “foreground ” (i.e., the part to be cut out) and “background ” (i.e., the rest); 2) soft constraint inputs: the user draws strokes on the mesh indicating the region which the cuts should be made nearby; and 3) hard constraint inputs: the marks which the cutting contour must pass. The algorithm uses feature sensitive metrics that are based on surface geometric properties and cognitive theory. The integration of the constrained random walks algorithm, the optimization process, the feature sensitive metrics and the varieties of user inputs makes the algorithm intuitive, flexible, and effective as well. The experimental examples show that the proposed cutting method is fast, reliable, and capable of producing good results reflecting user intention and geometric attributes. Index Terms—Computational geometry and object modeling, interaction techniques, geometric algorithms. 1