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Now showing 1 - 10 of 12
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    Building Construction Sets by Tiling Grammar Simplification
    (The Eurographics Association and John Wiley & Sons Ltd., 2016) Kalojanov, Javor; Wand, Michael; Slusallek, Philipp; Joaquim Jorge and Ming Lin
    This paper poses the problem of fabricating physical construction sets from example geometry: A construction set provides a small number of different types of building blocks from which the example model as well as many similar variants can be reassembled. This process is formalized by tiling grammars. Our core contribution is an approach for simplifying tiling grammars such that we obtain physically manufacturable building blocks of controllable granularity while retaining variability, i.e., the ability to construct many different, related shapes. Simplification is performed by sequences of two types of elementary operations: non-local joint edge collapses in the tile graphs reduce the granularity of the decomposition and approximate replacement operations reduce redundancy. We evaluate our method on abstract graph grammars in addition to computing several physical construction sets, which are manufactured using a commodity 3D printer.
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    Symmetry in Shapes - Theory and Practice
    (The Eurographics Association, 2013) Mitra, Niloy; Ovsjanikov, Maksim; Pauly, Mark; Wand, Michael; Ceylan, Duygu; Diego Gutierrez and Karol Myszkowski
    Part I: What is symmetry? Part II: Extrinsic symmetry detection Part III: Intrinsic symmetries Part IV: Representations and applications Conclusions, wrap-up
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    Mutable Elastic Models for Sculpting Structured Shapes
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Milliez, Antoine; Wand, Michael; Cani, Marie-Paule; Seidel, Hans-Peter; I. Navazo, P. Poulin
    In this paper, we propose a new paradigm for free-form shape deformation. Standard deformable models minimize an energy measuring the distance to a single target shape. We propose a new, ''mutable'' elastic model. It represents complex geometry by a collection of parts and measures the distance of each part measures to a larger set of alternative rest configurations. By detecting and reacting to local switches between best-matching rest states, we build a 3D sculpting system: It takes a structured shape consisting of parts and replacement rules as input. The shape can subsequently be elongated, compressed, bent, cut, and merged within a constraints-based free-form editing interface, where alternative rest-states model to such changes. In practical experiments, we show that the approach yields a surprisingly intuitive and easy to implement interface for interactively designing objects described by such discrete shape grammars, for which direct shape control mechanisms were typically lacking.
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    Shape Analysis with Subspace Symmetries
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Berner, Alexander; Wand, Michael; Mitra, Niloy J.; Mewes, Daniel; Seidel, Hans-Peter; M. Chen and O. Deussen
    We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising.
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    Dynamic Geometry Processing
    (The Eurographics Association, 2012) Chang, Will; Li, Hao; Mitra, Niloy; Pauly, Mark; Wand, Michael; Renato Pajarola and Michela Spagnuolo
    Throughout the last few years, acquisition and processing of dynamic geometry has already received quite an amount of attention in the computer vision and graphics research community. Recently, the topic has gained a significant boost due to the availability of commodity devices for dynamic geometry acquisition: The introduction of the Microsoft ''Kinect'' device made this kind of technology broadly available, being very well received by both researchers and end-users, and even more development in this direction can probably be expected for the near future. The tutorial on ''Dynamic Geometry Processing'' considers the problem of processing such dynamic range data effectively and efficiently. The tutorial introduces basic processing techniques for analyzing and matching range data. It introduces models for correspondence estimation and presents the according basic algorithmic building blocks. Furthermore, it discusses the current state-of-the-art by looking at example approaches for processing and real-time tracking of dynamic data. In addition, the tutorial will also identify and discuss future challenges in the field, aiming at inspiring future work in this exciting area of research.
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    Computing Correspondences in Geometric Data Sets
    (The Eurographics Association, 2011) Chang, Will; Li, Hao; Mitra, Niloy; Pauly, Mark; Rusinkiewicz, Szymon; Wand, Michael; Ralph Martin and Juan Carlos Torres
    Shape registration and, more generally speaking,computing correspondence across shapes are fundamental problems in computer graphics and vision. Problems from this area show up in many different variants such as scan registration, deformable shapematching, animation reconstruction, or finding partial symmetries of objects. Computing correspondences is a main prerequisite for higher level shape processing algorithms, such as building statistical models, non-local denoising, or inverse procedural modeling. Our tutorial addresses correspondence problems in geometric shapes. We will look at the problem from two different perspectives: In the first part of our tutorial, we will motivate the problem and explain the problem structure (formal models for shape matching), its variants (partial vs. complete matching, deformable vs. rigid, etc) and specific challenges (such as noise, incomplete data, and statistical descriptions thereof). In the second part, we will look at algorithms for solving these problems, and at applications of these. Again, we will focus on the main ideas and principles. Our overall goal is to give the attendee a "coordinate system" of the field, to convey the main problem structure and the main approaches to solve the problem, as well as open questions and research challenges. Topics covered will include rigid and deformable shape matching, local and global correspondence algorithms, as well as symmetry detection and applications.
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    Intrinsic Shape Matching by Planned Landmark Sampling
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Tevs, Art; Berner, Alexander; Wand, Michael; Ihrke, Ivo; Seidel, Hans-Peter; M. Chen and O. Deussen
    Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling-based shape matching algorithm that uses a planning step to find optimized "landmark" points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.
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    Learning Line Features in 3D Geometry
    (The Eurographics Association and Blackwell Publishing Ltd., 2011) Sunkel, Martin; Jansen, Silke; Wand, Michael; Eisemann, Elmar; Seidel, Hans-Peter; M. Chen and O. Deussen
    Feature detection in geometric datasets is a fundamental tool for solving shape matching problems such as partial symmetry detection. Traditional techniques usually employ a priori models such as crease lines that are unspecific to the actual application. Our paper examines the idea of learning geometric features. We introduce a formal model for a class of linear feature constellations based on a Markov chain model and propose a novel, efficient algorithm for detecting a large number of features simultaneously. After a short user-guided training stage, in which one or a few example lines are sketched directly onto the input data, our algorithm automatically finds all pieces of geometry similar to the marked areas. In particular, the algorithm is able recognize larger classes of semantically similar but geometrically varying features, which is very difficult using unsupervised techniques. In a number of experiments, we apply our technique to point cloud data from 3D scanners. The algorithm is able to detect features with very low rates of false positives and negatives and to recognize broader classes of similar geometry (such as "windows" in a building scan) even from few training examples, thereby significantly improving over previous unsupervised techniques.
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    Structure-Aware Shape Processing
    (The Eurographics Association, 2013) Mitra, Niloy J.; Wand, Michael; Zhang, Hao; Cohen-Or, Daniel; Bokeloh, Martin; M. Sbert and L. Szirmay-Kalos
    Shape structure is about the arrangement and relations between shape parts. Structure-aware shape processing goes beyond local geometry and low level processing, and analyzes and processes shapes at a high level. It focuses more on the global inter and intra semantic relations among the parts of shape rather than on their local geometry. With recent developments in easy shape acquisition, access to vast repositories of 3D models, and simple-to-use desktop fabrication possibilities, the study of structure in shapes has become a central research topic in shape analysis, editing, and modeling. A whole new line of structure-aware shape processing algorithms has emerged that base their operation on an attempt to understand such structure in shapes. The algorithms broadly consist of two key phases: an analysis phase, which extracts structural information from input data; and a (smart) processing phase, which utilizes the extracted information for exploration, editing, and synthesis of novel shapes. In this survey paper, we organize, summarize, and present the key concepts and methodological approaches towards efficient structure-aware shape processing. We discuss common models of structure, their implementation in terms of mathematical formalism and algorithms, and explain the key principles in the context of a number of state-ofthe- art approaches. Further, we attempt to list the key open problems and challenges, both at the technical and at the conceptual level, to make it easier for new researchers to better explore and contribute to this topic. Our goal is to both give the practitioner an overview of available structure-aware shape processing techniques, as well as identify future research questions in this important, emerging, and fascinating research area.
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    A Correlated Parts Model for Object Detection in Large 3D Scans
    (The Eurographics Association and Blackwell Publishing Ltd., 2013) Sunkel, Martin; Jansen, Silke; Wand, Michael; Seidel, Hans-Peter; I. Navazo, P. Poulin
    This paper addresses the problem of detecting objects in 3D scans according to object classes learned from sparse user annotation. We model objects belonging to a class by a set of fully correlated parts, encoding dependencies between local shapes of different parts as well as their relative spatial arrangement. For an efficient and comprehensive retrieval of instances belonging to a class of interest, we introduce a new approximate inference scheme and a corresponding planning procedure. We extend our technique to hierarchical composite structures, reducing training effort and modeling spatial relations between detected instances. We evaluate our method on a number of real-world 3D scans and demonstrate its benefits as well as the performance of the new inference algorithm.