Showing papers on "Computational geometry published in 2016"

CGAL: the computational geometry algorithms library

Abstract: This course provides an overview of CGAL geometric algorithms and data structures. We start with a presentation the objectives and scope of the CGAL open source project. The three next parts cover SIGGRAPH topics: (1) CGAL for point set processing, including denoising, outlier removal, smoothing, resampling, curvature estimation, shape detection and surface reconstruction, (2) CGAL for polygon mesh processing, including Boolean operations, deformation, skeletonization, segmentation, hole filling, isotropic remeshing, simplification, and (3) CGAL for mesh generation, including surface and volume mesh generation, from either 3D images, implicit functions or surface meshes.

Computational Conformal Geometry

Abstract: This new volume presents thorough introductions to the theoretical foundations - as well as to the practical algorithms - of computational conformal geometry. These have direct applications to engineering and digital geometric processing, including surface parameterization, surface matching, brain mapping, 3-D face recognition and identification, facial expression and animation, dynamic face tracking, mesh-spline conversion, and more. Computational conformal geometry is an emerging inter-disciplinary field, with applications to algebraic topology, differential geometry and Riemann surface theories applied to geometric modeling, computer graphics, computer vision, medical imaging, visualization, scientific computation, and many other engineering fields.

3D Shape and Indirect Appearance by Structured Light Transport

Abstract: We consider the problem of deliberately manipulating the direct and indirect light flowing through a time-varying, general scene in order to simplify its visual analysis. Our approach rests on a crucial link between stereo geometry and light transport: while direct light always obeys the epipolar geometry of a projector-camera pair, indirect light overwhelmingly does not. We show that it is possible to turn this observation into an imaging method that analyzes light transport in real time in the optical domain, prior to acquisition. This yields three key abilities that we demonstrate in an experimental camera prototype: (1) producing a live indirect-only video stream for any scene, regardless of geometric or photometric complexity; (2) capturing images that make existing structured-light shape recovery algorithms robust to indirect transport; and (3) turning them into one-shot methods for dynamic 3D shape capture.

Variational principles for Minkowski type problems, discrete optimal transport, and discrete Monge–Ampère equations

Abstract: In this paper, we develop several related finite dimensional variational principles for discrete optimal transport (DOT), Minkowski type problems for convex polytopes and discrete Monge-Ampere equation (DMAE). A link between the discrete optimal transport, discrete Monge-Ampere equation and the power diagram in computational geometry is established.

Fast and exact discrete geodesic computation based on triangle-oriented wavefront propagation

Abstract: Computing discrete geodesic distance over triangle meshes is one of the fundamental problems in computational geometry and computer graphics. In this problem, an effective window pruning strategy can significantly affect the actual running time. Due to its importance, we conduct an in-depth study of window pruning operations in this paper, and produce an exhaustive list of scenarios where one window can make another window partially or completely redundant. To identify a maximal number of redundant windows using such pairwise cross checking, we propose a set of procedures to synchronize local window propagation within the same triangle by simultaneously propagating a collection of windows from one triangle edge to its two opposite edges. On the basis of such synchronized window propagation, we design a new geodesic computation algorithm based on a triangle-oriented region growing scheme. Our geodesic algorithm can remove most of the redundant windows at the earliest possible stage, thus significantly reducing computational cost and memory usage at later stages. In addition, by adopting triangles instead of windows as the primitive in propagation management, our algorithm significantly cuts down the data management overhead. As a result, it runs 4--15 times faster than MMP and ICH algorithms, 2-4 times faster than FWP-MMP and FWP-CH algorithms, and also incurs the least memory usage.

Geometric Modeling and Mesh Generation from Scanned Images

Abstract: Cutting-Edge Techniques to Better Analyze and Predict Complex Physical Phenomena Geometric Modeling and Mesh Generation from Scanned Images shows how to integrate image processing, geometric modeling, and mesh generation with the finite element method (FEM) to solve problems in computational biology, medicine, materials science, and engineering. Based on the authors recent research and course at Carnegie Mellon University, the text explains the fundamentals of medical imaging, image processing, computational geometry, mesh generation, visualization, and finite element analysis. It also explores novel and advanced applications in computational biology, medicine, materials science, and other engineering areas. One of the first to cover this emerging interdisciplinary field, the book addresses biomedical/material imaging, image processing, geometric modeling and visualization, FEM, and biomedical and engineering applications. It introduces image-mesh-simulation pipelines, reviews numerical methods used in various modules of the pipelines, and discusses several scanning techniques, including ones to probe polycrystalline materials. The book next presents the fundamentals of geometric modeling and computer graphics, geometric objects and transformations, and curves and surfaces as well as two isocontouring methods: marching cubes and dual contouring. It then describes various triangular/tetrahedral and quadrilateral/hexahedral mesh generation techniques. The book also discusses volumetric T-spline modeling for isogeometric analysis (IGA) and introduces some new developments of FEM in recent years with applications.

Clustering problems on sliding windows

Abstract: We explore clustering problems in the streaming sliding window model in both general metric spaces and Euclidean space. We present the first polylogarithmic space O(1)-approximation to the metric k-median and metric k-means problems in the sliding window model, answering the main open problem posed by Babcock, Datar, Motwani and O'Callaghan [5], which has remained unanswered for over a decade. Our algorithm uses O(k3log6W) space and poly(k, log W) update time, where W is the window size. This is an exponential improvement on the space required by the technique due to Babcock, et al. We introduce a data structure that extends smooth histograms as introduced by Braverman and Ostrovsky [11] to operate on a broader class of functions. In particular, we show that using only polylogarithmic space we can maintain a summary of the current window from which we can construct an O(1)-approximate clustering solution.Merge-and-reduce is a generic method in computational geometry for adapting offline algorithms to the insertion-only streaming model. Several well-known coreset constructions are maintainable in the insertion-only streaming model using this method, including well-known coreset techniques for the k-median and k-means problems in both low-and high-dimensional Euclidean spaces [31, 15]. Previous work [27] has adapted coreset techniques to the insertion-deletion model, but translating them to the sliding window model has remained a challenge. We give the first algorithm that, given an insertion-only streaming coreset of space s (maintained using merge-and-reduce method), maintains this coreset in the sliding window model using O(s2e--2 log W) space.For clustering problems, our results constitute the first significant step towards resolving problem number 20 from the List of Open Problems in Sublinear Algorithms [39].

Optimal Deterministic Algorithms for 2-d and 3-d Shallow Cuttings

Abstract: We present optimal deterministic algorithms for constructing shallow cuttings in an arrangement of lines in two dimensions or planes in three dimensions. Our results improve the deterministic polynomial-time algorithm of Matousek (Comput Geom 2(3):169---186, 1992) and the optimal but randomized algorithm of Ramos (Proceedings of the Fifteenth Annual Symposium on Computational Geometry, SoCG'99, 1999). This leads to efficient derandomization of previous algorithms for numerous well-studied problems in computational geometry, including halfspace range reporting in 2-d and 3-d, k nearest neighbors search in 2-d, $$({\le }k)$$(≤k)-levels in 3-d, order-k Voronoi diagrams in 2-d, linear programming with k violations in 2-d, dynamic convex hulls in 3-d, dynamic nearest neighbor search in 2-d, convex layers (onion peeling) in 3-d, $$\varepsilon $$?-nets for halfspace ranges in 3-d, and more. As a side product we also describe an optimal deterministic algorithm for constructing standard (non-shallow) cuttings in two dimensions, which is arguably simpler than the known optimal algorithms by Matousek (Discrete Comput Geom 6(1):385---406, 1991) and Chazelle (Discrete Comput Geom 9(1):145---158, 1993).

Parallelism in Randomized Incremental Algorithms

Abstract: In this paper we show that most sequential randomized incremental algorithms are in fact parallel. We consider several random incremental algorithms including algorithms for comparison sorting and Delaunay triangulation; linear programming, closest pair, and smallest enclosing disk in constant dimensions; as well as least-element lists and strongly connected components on graphs.We analyze the dependence between iterations in an algorithm, and show that the dependence structure is shallow for all of the algorithms, implying high parallelism. We identify three types of dependences found in the algorithms studied and present a framework for analyzing each type of algorithm. Using the framework gives work-efficient polylogarithmic-depth parallel algorithms for most of the problems that we study. Some of these algorithms are straightforward (e.g., sorting and linear programming), while others are more novel and require more effort to obtain the desired bounds (e.g., Delaunay triangulation and strongly connected components). The most surprising of these results is for planar Delaunay triangulation for which the incremental approach is by far the most commonly used in practice, but for which it was not previously known whether it is theoretically efficient in parallel.

The geometry of homothetic covering and illumination

Abstract: At a first glance, the problem of illuminating the boundary of a convex body by external light sources and the problem of covering a convex body by its smaller positive homothetic copies appear to be quite different. They are in fact two sides of the same coin and give rise to one of the important longstanding open problems in discrete geometry, namely, the Illumination Conjecture. In this paper, we survey the activity in the areas of discrete geometry, computational geometry and geometric analysis motivated by this conjecture. Special care is taken to include the recent advances that are not covered by the existing surveys. We also include some of our recent results related to these problems and describe two new approaches – one conventional and the other computer-assisted – to make progress on the illumination problem. Some open problems and conjectures are also presented.

Solving some vector subset problems by Voronoi diagrams

Abstract: We propose a general approach to solving some vector subset problems in a Euclidean space that is based on higher-order Voronoi diagrams. In the case of a fixed space dimension, this approach allows us to find optimal solutions to these problems in polynomial time which is better than the runtime of available algorithms.

Open Research Areas in Distance Geometry

Abstract: Distance geometry is based on the inverse problem that asks to find the positions of points, in a Euclidean space of given dimension, that are compatible with a given set of distances. We briefly introduce the field, and discuss some open and promising research areas.

Parallel Algorithms for Constructing Range and Nearest-Neighbor Searching Data Structures

Abstract: With the massive amounts of data available today, it is common to store and process data using multiple machines. Parallel programming platforms such as MapReduce and its variants are popular frameworks for handling such large data. We present the first provably efficient algorithms to compute, store, and query data structures for range queries and approximate nearest neighbor queries in a popular parallel computing abstraction that captures the salient features of MapReduce and other massively parallel communication (MPC) models. In particular, we describe algorithms for $kd$-trees, range trees, and BBD-trees that only require O(1) rounds of communication for both preprocessing and querying while staying competitive in terms of running time and workload to their classical counterparts. Our algorithms are randomized, but they can be made deterministic at some increase in their running time and workload while keeping the number of rounds of communication to be constant.

Capstone: A Geometry-Centric Platform to Enable Physics-Based Simulation and System Design

Abstract: Capstone is a geometry-centric platform for a unified representation of the geometry, mesh, and attribution needed for engineering analyses with varying fidelity. Meshes and attributes are both associated with a robust mathematical model of the geometry, enabling any change to be automatically propagated to the meshes and attributes needed for analyses. Captsone provides a software platform with well-abstracted and compact interfaces to create, modify, and query geometry, mesh, and attribution information for a model. This forms a foundation for geometry-based design environments and solvers that can access the geometry at runtime for scalable and accurate a posteriori mesh adaptation. Capstone provides a graphical front end for computational fluid dynamics. Capstone is being used and evaluated by several US Department of Defense organizations.

The geometry of homothetic covering and illumination

Abstract: At a first glance, the problem of illuminating the boundary of a convex body by external light sources and the problem of covering a convex body by its smaller positive homothetic copies appear to be quite different. They are in fact two sides of the same coin and give rise to one of the important longstanding open problems in discrete geometry, namely, the Illumination Conjecture. In this paper, we survey the activity in the areas of discrete geometry, computational geometry and geometric analysis motivated by this conjecture. Special care is taken to include the recent advances that are not covered by the existing surveys. We also include some of our recent results related to these problems and describe two new approaches -- one conventional and the other computer-assisted -- to make progress on the illumination problem. Some open problems and conjectures are also presented.

Interactive modeling of mechanical objects

Abstract: Objects with various types of mechanical joints are among the most commonly built. Joints implement a vocabulary of simple constrained motions (kinematic pairs) that can be used to build more complex behaviors. Defining physically correct joint geometry is crucial both for realistic appearance of models during motion, as these are typically the only parts of geometry that stay in contact, and for fabrication. Direct design of joint geometry often requires more effort than the design of the rest of the object geometry, as it requires design of components that stay in precise contact, are aligned with other parts, and allow the desired range of motion. We present an interactive system for creating physically realizable joints with user-controlled appearance. Our system minimizes or, in most cases, completely eliminates the need for the user to manipulate low-level geometry of joints. This is achieved by automatically inferring a small number of plausible combinations of joint dimensions, placement and orientation from part geometry, with the user making the final high-level selection based on object semantic. Through user studies, we demonstrate that functional results with a satisfying appearance can be obtained quickly by users with minimal modeling experience, offering a significant improvement in the time required for joint construction, compared to standard modeling approaches.

How Good is the Chord Algorithm

Abstract: The Chord algorithm is a popular, simple method for the succinct approximation of curves, which is widely used, under different names, in a variety of areas, such as multiobjective and parametric optimization, computational geometry, and graphics. We analyze the performance of the Chord algorithm, as compared to the optimal approximation that achieves a desired accuracy with the minimum number of points. We prove sharp upper and lower bounds, both in the worst case and average case settings.

How Good is the Chord Algorithm

Abstract: The Chord algorithm is a popular, simple method for the succinct approximation of curves, which is widely used, under different names, in a variety of areas, such as multiobjective and parametric optimization, computational geometry, and graphics. We analyze the performance of the Chord algorithm, as compared to the optimal approximation that achieves a desired accuracy with the minimum number of points. We prove sharp upper and lower bounds, both in the worst case and average case settings.

Rapid geometry definition for multidisciplinary design and analysis of an aircraft

Abstract: Conceptual and preliminary design level of aircraft design is searching for an easy, flexible and efficient way of computational geometry definition. Accelerating the process of geometry definition is the basic step for acceleration of all computations. It also enables optimization, where changes of numerical model are made automatically according to the optimization algorithms. The geometry definition has to be robust, free from errors and stay feasible.

Near-isometric level set tracking

Abstract: Implicit representations of geometry have found applications in shape modeling, simulation, and other graphics pipelines. These representations, however, do not provide information about the paths of individual points as shapes move and undergo deformation. For this reason, we reconsider the problem of tracking points on level set surfaces, with the goal of designing an algorithm that --- unlike previous work --- can recover rotational motion and nearly isometric deformation. We track points on level sets of a time-varying function using approximate Killing vector fields (AKVFs), the velocity fields of near-isometric motions. To this end, we provide suitable theoretical and discrete constructions for computing AKVFs in a narrow band surrounding an animated level set surface. Furthermore, we propose time integrators well-suited to integrating AKVFs in time to track points. We demonstrate the theoretical and practical advantages of our proposed algorithms on synthetic and practical tasks.

Implementation of linear minimum area enclosing triangle algorithm

Abstract: An algorithm which computes the minimum area triangle enclosing a convex polygon in linear time already exists in the literature. The paper describing the algorithm also proves that the provided solution is optimal and a lower complexity sequential algorithm cannot exist. However, only a high-level description of the algorithm was provided, making the implementation difficult to reproduce. The present note aims to contribute to the field by providing a detailed description of the algorithm which is easy to implement and reproduce, and a benchmark comprising 10,000 variable sized, randomly generated convex polygons for illustrating the linearity of the algorithm.

Adapting feature curve networks to a prescribed scale

Abstract: Feature curves on surface meshes are usually defined solely based on local shape properties such as dihedral angles and principal curvatures. From the application perspective, however, the meaningfulness of a network of feature curves also depends on a global scale parameter that takes the distance between feature curves into account, i.e., on a coarse scale, nearby feature curves should be merged or suppressed if the surface region between them is not representable at the given scale/resolution. In this paper, we propose a computational approach to the intuitive notion of scale conforming feature curve networks where the density of feature curves on the surface adapts to a global scale parameter. We present a constrained global optimization algorithm that computes scale conforming feature curve networks by eliminating curve segments that represent surface features, which are not compatible to the prescribed scale. To demonstrate the usefulness of our approach we apply isotropic and anisotropic remeshing schemes that take our feature curve networks as input. For a number of example meshes, we thus generate high quality shape approximations at various levels of detail.

Efficient delaunay tessellation through K-D tree decomposition

Abstract: Delaunay tessellations are fundamental data structures in computational geometry. They are important in data analysis, where they can represent the geometry of a point set or approximate its density. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We evaluate the new algorithm using two late-stage cosmology datasets. The new running times are up to 50 times faster using k-d tree compared with regular grid decomposition. Moreover, in the unbalanced data sets, decomposing the domain into a k-d tree is up to five times faster than decomposing it into a regular grid.