3,883 research outputs found

    QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

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    QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection

    BVH์™€ ํ† ๋Ÿฌ์Šค ํŒจ์น˜๋ฅผ ์ด์šฉํ•œ ๊ณก๋ฉด ๊ต์ฐจ๊ณก์„  ์—ฐ์‚ฐ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021.8. ๊น€๋ช…์ˆ˜.๋‘ ๋ณ€์ˆ˜๋ฅผ ๊ฐ€์ง€๋Š” B-์Šคํ”Œ๋ผ์ธ ์ž์œ ๊ณก๋ฉด์˜ ๊ณก๋ฉด๊ฐ„ ๊ต์ฐจ๊ณก์„ ๊ณผ ์ž๊ฐ€ ๊ต์ฐจ๊ณก์„ , ๊ทธ๋ฆฌ๊ณ  ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ๊ณก์„ ์„ ๊ตฌํ•˜๋Š” ํšจ์œจ์ ์ด๊ณ  ์•ˆ์ •์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•˜๋Š” ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์€ ์ตœํ•˜๋‹จ ๋…ธ๋“œ์— ์ตœ๋Œ€ ์ ‘์ด‰ ํ† ๋Ÿฌ์Šค๋ฅผ ๊ฐ€์ง€๋Š” ๋ณตํ•ฉ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ตฌ์กฐ์— ๊ธฐ๋ฐ˜์„ ๋‘๊ณ  ์žˆ๋‹ค. ์ด ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ตฌ์กฐ๋Š” ๊ณก๋ฉด๊ฐ„ ๊ต์ฐจ๋‚˜ ์ž๊ฐ€ ๊ต์ฐจ๊ฐ€ ๋ฐœ์ƒํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๋Š” ์ž‘์€ ๊ณก๋ฉด ์กฐ๊ฐ ์Œ๋“ค์˜ ๊ธฐํ•˜ํ•™์  ๊ฒ€์ƒ‰์„ ๊ฐ€์†ํ™”ํ•œ๋‹ค. ์ตœ๋Œ€ ์ ‘์ด‰ ํ† ๋Ÿฌ์Šค๋Š” ์ž๊ธฐ๊ฐ€ ๊ทผ์‚ฌํ•œ C2-์—ฐ์† ์ž์œ ๊ณก๋ฉด๊ณผ 2์ฐจ ์ ‘์ด‰์„ ๊ฐ€์ง€๋ฏ€๋กœ ์ฃผ์–ด์ง„ ๊ณก๋ฉด์—์„œ ๋‹ค์–‘ํ•œ ๊ธฐํ•˜ ์—ฐ์‚ฐ์˜ ์ •๋ฐ€๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๋Š”๋ฐ ํ•„์ˆ˜์ ์ธ ์—ญํ• ์„ ํ•œ๋‹ค. ํšจ์œจ์ ์ธ ๊ณก๋ฉด๊ฐ„ ๊ต์ฐจ๊ณก์„  ๊ณ„์‚ฐ์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด, ๋ฏธ๋ฆฌ ๋งŒ๋“ค์–ด์ง„, ์ตœํ•˜๋‹จ ๋…ธ๋“œ์— ์ตœ๋Œ€ ์ ‘์ด‰ ํ† ๋Ÿฌ์Šค๊ฐ€ ์žˆ์œผ๋ฉฐ ๊ตฌํ˜•๊ตฌ๋ฉด ํŠธ๋ฆฌ๋ฅผ ๊ฐ€์ง€๋Š” ๋ณตํ•ฉ ์ดํ•ญ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ตœ๋Œ€ ์ ‘์ด‰ ํ† ๋Ÿฌ์Šค๋Š” ๊ฑฐ์˜ ๋ชจ๋“  ๊ณณ์—์„œ ์ ‘์„ ๊ต์ฐจ๊ฐ€ ๋ฐœ์ƒํ•˜๋Š”, ์ž๋ช…ํ•˜์ง€ ์•Š์€ ๊ณก๋ฉด๊ฐ„ ๊ต์ฐจ๊ณก์„  ๊ณ„์‚ฐ ๋ฌธ์ œ์—์„œ๋„ ํšจ์œจ์ ์ด๊ณ  ์•ˆ์ •์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์„ ๊ตฌํ•˜๋Š” ๋ฌธ์ œ๋Š” ์ฃผ๋กœ ๋งˆ์ดํ„ฐ ์  ๋•Œ๋ฌธ์— ๊ณก๋ฉด๊ฐ„ ๊ต์ฐจ๊ณก์„ ์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ ๋ณด๋‹ค ํ›จ์”ฌ ๋” ์–ด๋ ต๋‹ค. ์ž๊ฐ€ ๊ต์ฐจ ๊ณก๋ฉด์€ ๋งˆ์ดํ„ฐ ์  ๋ถ€๊ทผ์—์„œ ๋ฒ•์„  ๋ฐฉํ–ฅ์ด ๊ธ‰๊ฒฉํžˆ ๋ณ€ํ•˜๋ฉฐ, ๋งˆ์ดํ„ฐ ์ ์€ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์˜ ๋์ ์— ์œ„์น˜ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋งˆ์ดํ„ฐ ์ ์€ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก๋ฉด์˜ ๊ธฐํ•˜ ์—ฐ์‚ฐ ์•ˆ์ •์„ฑ์— ํฐ ๋ฌธ์ œ๋ฅผ ์ผ์œผํ‚จ๋‹ค. ๋งˆ์ดํ„ฐ ์ ์„ ์•ˆ์ •์ ์œผ๋กœ ๊ฐ์ง€ํ•˜์—ฌ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์˜ ๊ณ„์‚ฐ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•ด, ์ž์œ ๊ณก๋ฉด์„ ์œ„ํ•œ ๋ณตํ•ฉ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ตฌ์กฐ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์‚ผํ•ญ ํŠธ๋ฆฌ ๊ตฌ์กฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ํŠนํžˆ, ๋‘ ๋ณ€์ˆ˜๋ฅผ ๊ฐ€์ง€๋Š” ๊ณก๋ฉด์˜ ๋งค๊ฐœ๋ณ€์ˆ˜์˜์—ญ์—์„œ ๋งˆ์ดํ„ฐ ์ ์„ ์ถฉ๋ถ„ํžˆ ์ž‘์€ ์‚ฌ๊ฐํ˜•์œผ๋กœ ๊ฐ์‹ธ๋Š” ํŠน๋ณ„ํ•œ ํ‘œํ˜„ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ ‘์„ ๊ต์ฐจ์™€ ๋งˆ์ดํ„ฐ ์ ์„ ๊ฐ€์ง€๋Š”, ์•„์ฃผ ์ž๋ช…ํ•˜์ง€ ์•Š์€ ์ž์œ ๊ณก๋ฉด ์˜ˆ์ œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ƒˆ ๋ฐฉ๋ฒ•์ด ํšจ๊ณผ์ ์ž„์„ ์ž…์ฆํ•œ๋‹ค. ๋ชจ๋“  ์‹คํ—˜ ์˜ˆ์ œ์—์„œ, ๊ธฐํ•˜์š”์†Œ๋“ค์˜ ์ •ํ™•๋„๋Š” ํ•˜์šฐ์Šค๋„๋ฅดํ”„ ๊ฑฐ๋ฆฌ์˜ ์ƒํ•œ๋ณด๋‹ค ๋‚ฎ์Œ์„ ์ธก์ •ํ•˜์˜€๋‹ค.We present a new approach to the development of efficient and stable algorithms for intersecting freeform surfaces, including the surface-surface-intersection and the surface self-intersection of bivariate rational B-spline surfaces. Our new approach is based on a hybrid Bounding Volume Hierarchy(BVH) that stores osculating toroidal patches in the leaf nodes. The BVH structure accelerates the geometric search for the potential pairs of local surface patches that may intersect or self-intersect. Osculating toroidal patches have second-order contact with C2-continuous freeform surfaces that they approximate, which plays an essential role in improving the precision of various geometric operations on the given surfaces. To support efficient computation of the surface-surface-intersection curve, we design a hybrid binary BVH that is basically a pre-built Rectangle-Swept Sphere(RSS) tree enhanced with osculating toroidal patches in their leaf nodes. Osculating toroidal patches provide efficient and robust solutions to the problem even in the non-trivial cases of handling two freeform surfaces intersecting almost tangentially everywhere. The surface self-intersection problem is considerably more difficult than computing the intersection of two different surfaces, mainly due to the existence of miter points. A self-intersecting surface changes its normal direction dramatically around miter points, located at the open endpoints of the self-intersection curve. This undesirable behavior causes serious problems in the stability of geometric algorithms on self-intersecting surfaces. To facilitate surface self-intersection computation with a stable detection of miter points, we propose a ternary tree structure for the hybrid BVH of freeform surfaces. In particular, we propose a special representation of miter points using sufficiently small quadrangles in the parameter domain of bivariate surfaces and expand ideas to offset surfaces. We demonstrate the effectiveness of the proposed new approach using some highly non-trivial examples of freeform surfaces with tangential intersections and miter points. In all the test examples, the closeness of geometric entities is measured under the Hausdorff distance upper bound.Chapter 1 Introduction 1 1.1 Background 1 1.2 Surface-Surface-Intersection 5 1.3 Surface Self-Intersection 8 1.4 Main Contribution 12 1.5 Thesis Organization 14 Chapter 2 Preliminaries 15 2.1 Differential geometry of surfaces 15 2.2 Bezier curves and surfaces 17 2.3 Surface approximation 19 2.4 Torus 21 2.5 Summary 24 Chapter 3 Previous Work 25 3.1 Surface-Surface-Intersection 25 3.2 Surface Self-Intersection 29 3.3 Summary 32 Chapter 4 Bounding Volume Hierarchy for Surface Intersections 33 4.1 Binary Structure 33 4.1.1 Hierarchy of Bilinear Surfaces 34 4.1.2 Hierarchy of Planar Quadrangles 37 4.1.3 Construction of Leaf Nodes with Osculating Toroidal Patches 41 4.2 Ternary Structure 44 4.2.1 Miter Points 47 4.2.2 Leaf Nodes 50 4.2.3 Internal Nodes 51 4.3 Summary 56 Chapter 5 Surface-Surface-Intersection 57 5.1 BVH Traversal 58 5.2 Construction of SSI Curve Segments 59 5.2.1 Merging SSI Curve Segments with G1-Biarcs 60 5.2.2 Measuring the SSI Approximation Error Using G1-Biarcs 63 5.3 Tangential Intersection 64 5.4 Summary 65 Chapter 6 Surface Self-Intersection 67 6.1 Preprocessing 68 6.2 BVH Traversal 69 6.3 Construction of Intersection Curve Segments 70 6.4 Summary 72 Chapter 7 Trimming Offset Surfaces with Self-Intersection Curves 74 7.1 Offset Surface and Ternary Hybrid BVH 75 7.2 Preprocessing 77 7.3 Merging Intersection Curve Segments 81 7.4 Summary 84 Chapter 8 Experimental Results 85 8.1 Surface-Surface-Intersection 85 8.2 Surface Self-Intersection 97 8.2.1 Regular Surfaces 97 8.2.2 Offset Surfaces 100 Chapter 9 Conclusion 106 Bibliography 108 ์ดˆ๋ก 120๋ฐ•

    QuickCSG: Fast Arbitrary Boolean Combinations of N Solids

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    QuickCSG computes the result for general N-polyhedron boolean expressions without an intermediate tree of solids. We propose a vertex-centric view of the problem, which simplifies the identification of final geometric contributions, and facilitates its spatial decomposition. The problem is then cast in a single KD-tree exploration, geared toward the result by early pruning of any region of space not contributing to the final surface. We assume strong regularity properties on the input meshes and that they are in general position. This simplifying assumption, in combination with our vertex-centric approach, improves the speed of the approach. Complemented with a task-stealing parallelization, the algorithm achieves breakthrough performance, one to two orders of magnitude speedups with respect to state-of-the-art CPU algorithms, on boolean operations over two to dozens of polyhedra. The algorithm also outperforms GPU implementations with approximate discretizations, while producing an output without redundant facets. Despite the restrictive assumptions on the input, we show the usefulness of QuickCSG for applications with large CSG problems and strong temporal constraints, e.g. modeling for 3D printers, reconstruction from visual hulls and collision detection

    Using implicit equations of parametric curves and surfaces without computing them: Polynomial algebra by values

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    The availability of the implicit equation of a plane curve or of a 3D surface can be very useful in order to solve many geometric problems involving the considered curve or surface: for example, when dealing with the point position problem or answering intersection questions. On the other hand, it is well known that in most cases, even for moderate degrees, the implicit equation is either difficult to compute or, if computed, the high degree and the big size of the coefficients makes extremely difficult its use in practice. We will show that, for several problems involving plane curves, 3D surfaces and some of their constructions (for example, offsets), it is possible to use the implicit equation (or, more precisely, its properties) without needing to explicitly determine it. We replace the computation of the implicit equation with the evaluation of the considered parameterizations in a set of points. We then translate the geometric problem in hand, into one or several generalized eigenvalue problems on matrix pencils (depending again on several evaluations of the considered parameterizations). This is the so-called โ€œpolynomial algebra by valuesโ€ approach where the huge polynomial equations coming from Elimination Theory (e.g., using resultants) are replaced by big structured and sparse numerical matrices. For these matrices there are well-known numerical techniques allowing to provide the results we need to answer the geometric questions on the considered curves and surfaces

    Doctor of Philosophy

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    dissertationVolumetric parameterization is an emerging field in computer graphics, where volumetric representations that have a semi-regular tensor-product structure are desired in applications such as three-dimensional (3D) texture mapping and physically-based simulation. At the same time, volumetric parameterization is also needed in the Isogeometric Analysis (IA) paradigm, which uses the same parametric space for representing geometry, simulation attributes and solutions. One of the main advantages of the IA framework is that the user gets feedback directly as attributes of the NURBS model representation, which can represent geometry exactly, avoiding both the need to generate a finite element mesh and the need to reverse engineer the simulation results from the finite element mesh back into the model. Research in this area has largely been concerned with issues of the quality of the analysis and simulation results assuming the existence of a high quality volumetric NURBS model that is appropriate for simulation. However, there are currently no generally applicable approaches to generating such a model or visualizing the higher order smooth isosurfaces of the simulation attributes, either as a part of current Computer Aided Design or Reverse Engineering systems and methodologies. Furthermore, even though the mesh generation pipeline is circumvented in the concept of IA, the quality of the model still significantly influences the analysis result. This work presents a pipeline to create, analyze and visualize NURBS geometries. Based on the concept of analysis-aware modeling, this work focusses in particular on methodologies to decompose a volumetric domain into simpler pieces based on appropriate midstructures by respecting other relevant interior material attributes. The domain is decomposed such that a tensor-product style parameterization can be established on the subvolumes, where the parameterization matches along subvolume boundaries. The volumetric parameterization is optimized using gradient-based nonlinear optimization algorithms and datafitting methods are introduced to fit trivariate B-splines to the parameterized subvolumes with guaranteed order of accuracy. Then, a visualization method is proposed allowing to directly inspect isosurfaces of attributes, such as the results of analysis, embedded in the NURBS geometry. Finally, the various methodologies proposed in this work are demonstrated on complex representations arising in practice and research

    ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ฒ€์ถœ ๋ฐ ์ œ๊ฑฐ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ๊น€๋ช…์ˆ˜.Offset curves and surfaces have many applications in computer-aided design and manufacturing, but the self-intersections and redundancies must be trimmed away for their practical use. We present a new method for offset curve and surface trimming that detects the self-intersections and eliminates the redundant parts of an offset curve and surface that are closer than the offset distance to the original curve and surface. We first propose an offset trimming method based on constructing geometric constraint equations. We formulate the constraint equations of the self-intersections of an offset curve and surface in the parameter domain of the original curve and surface. Numerical computations based on the regularity and intrinsic properties of the given input curve and surface is carried out to compute the solution of the constraint equations. The method deals with numerical instability around near-singular regions of an offset surface by using osculating tori that can be constructed in a highly stable way, i.e., by offsetting the osculating torii of the given input regular surface. We reveal the branching structure and the terminal points from the complete self-intersection curves of the offset surface. From the observation that the trimming method based on the multivariate equation solving is computationally expensive, we also propose an acceleration technique to trim an offset curve and surface. The alternative method constructs a bounding volume hierarchy specially designed to enclose the offset curve and surface and detects the self-collision of the bounding volumes instead. In the case of an offset surface, the thickness of the bounding volumes is indirectly determined based on the maximum deviations of the positions and the normals between the given input surface patches and their osculating tori. For further acceleration, the bounding volumes are pruned as much as possible during self-collision detection using various geometric constraints imposed on the offset surface. We demonstrate the effectiveness of the new trimming method using several non-trivial test examples of offset trimming. Lastly, we investigate the problem of computing the Voronoi diagram of a freeform surface using the offset trimming technique for surfaces. By trimming the offset surface with a gradually changing offset radius, we compute the boundary of the Voronoi cells that appear in the concave side of the given input surface. In particular, we interpret the singular and branching points of the self-intersection curves of the trimmed offset surfaces in terms of the boundary elements of the Voronoi diagram.์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์€ computer-aided design (CAD)์™€ computer-aided manufacturing (CAM)์—์„œ ๋„๋ฆฌ ์ด์šฉ๋˜๋Š” ์—ฐ์‚ฐ๋“ค ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ํ•˜์ง€๋งŒ ์‹ค์šฉ์ ์ธ ํ™œ์šฉ์„ ์œ„ํ•ด์„œ๋Š” ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์—์„œ ์ƒ๊ธฐ๋Š” ์ž๊ฐ€ ๊ต์ฐจ๋ฅผ ์ฐพ๊ณ  ์ด๋ฅผ ๊ธฐ์ค€์œผ๋กœ ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์—์„œ ์›๋ž˜์˜ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์— ๊ฐ€๊นŒ์šด ๋ถˆํ•„์š”ํ•œ ์˜์—ญ์„ ์ œ๊ฑฐํ•˜์—ฌ์•ผํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์—์„œ ์ƒ๊ธฐ๋Š” ์ž๊ฐ€ ๊ต์ฐจ๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ , ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์—์„œ ์ƒ๊ธฐ๋Š” ๋ถˆํ•„์š”ํ•œ ์˜์—ญ์„ ์ œ๊ฑฐํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์šฐ์„  ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ์ ๋“ค๊ณผ ๊ทธ ๊ต์ฐจ์ ๋“ค์ด ๊ธฐ์ธํ•œ ์›๋ž˜ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ ๋“ค์ด ์ด๋ฃจ๋Š” ํ‰๋ฉด ์ด๋“ฑ๋ณ€ ์‚ผ๊ฐํ˜• ๊ด€๊ณ„๋กœ๋ถ€ํ„ฐ ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ์ ์˜ ์ œ์•ฝ ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๋ฐฉ์ •์‹๋“ค์„ ์„ธ์šด๋‹ค. ์ด ์ œ์•ฝ์‹๋“ค์€ ์›๋ž˜ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ๋ณ€์ˆ˜ ๊ณต๊ฐ„์—์„œ ํ‘œํ˜„๋˜๋ฉฐ, ์ด ๋ฐฉ์ •์‹๋“ค์˜ ํ•ด๋Š” ๋‹ค๋ณ€์ˆ˜ ๋ฐฉ์ •์‹์˜ ํ•ด๋ฅผ ๊ตฌํ•˜๋Š” solver๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ตฌํ•œ๋‹ค. ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ๊ฒฝ์šฐ, ์›๋ž˜ ๊ณก๋ฉด์˜ ์ฃผ๊ณก๋ฅ  ์ค‘ ํ•˜๋‚˜๊ฐ€ ์˜คํ”„์…‹ ๋ฐ˜์ง€๋ฆ„์˜ ์—ญ์ˆ˜์™€ ๊ฐ™์„ ๋•Œ ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ๋ฒ•์„ ์ด ์ •์˜๊ฐ€ ๋˜์ง€ ์•Š๋Š” ํŠน์ด์ ์ด ์ƒ๊ธฐ๋Š”๋ฐ, ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์ด ์ด ๋ถ€๊ทผ์„ ์ง€๋‚  ๋•Œ๋Š” ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์˜ ๊ณ„์‚ฐ์ด ๋ถˆ์•ˆ์ •ํ•ด์ง„๋‹ค. ๋”ฐ๋ผ์„œ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์ด ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ํŠน์ด์  ๋ถ€๊ทผ์„ ์ง€๋‚  ๋•Œ๋Š” ์˜คํ”„์…‹ ๊ณก๋ฉด์„ ์ ‘์ด‰ ํ† ๋Ÿฌ์Šค๋กœ ์น˜ํ™˜ํ•˜์—ฌ ๋” ์•ˆ์ •๋œ ๋ฐฉ๋ฒ•์œผ๋กœ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์„ ๊ตฌํ•œ๋‹ค. ๊ณ„์‚ฐ๋œ ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์œผ๋กœ๋ถ€ํ„ฐ ๊ต์ฐจ ๊ณก์„ ์˜ xyzxyz-๊ณต๊ฐ„์—์„œ์˜ ๋ง๋‹จ ์ , ๊ฐ€์ง€ ๊ตฌ์กฐ ๋“ฑ์„ ๋ฐํžŒ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๋˜ํ•œ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ธฐ๋ฐ˜์˜ ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„  ๊ฒ€์ถœ์„ ๊ฐ€์†ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ์€ ๊ธฐ์ € ๊ณก์„  ๋ฐ ๊ณก๋ฉด์„ ๋‹จ์ˆœํ•œ ๊ธฐํ•˜๋กœ ๊ฐ์‹ธ๊ณ  ๊ธฐํ•˜ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ๊ฐ€์†ํ™”์— ๊ธฐ์—ฌํ•œ๋‹ค. ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„ ์„ ๊ตฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๋ณธ ๋…ผ๋ฌธ์€ ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ๊ตฌ์กฐ๋ฅผ ๊ธฐ์ € ๊ณก๋ฉด์˜ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ๊ณผ ๊ธฐ์ € ๊ณก๋ฉด์˜ ๋ฒ•์„  ๊ณก๋ฉด์˜ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ์˜ ๊ตฌ์กฐ๋กœ๋ถ€ํ„ฐ ๊ณ„์‚ฐํ•˜๋ฉฐ ์ด๋•Œ ๊ฐ ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ์˜ ๋‘๊ป˜๋ฅผ ๊ณ„์‚ฐํ•œ๋‹ค. ๋˜ํ•œ, ๋ฐ”์šด๋”ฉ ๋ณผ๋ฅจ ์ค‘์—์„œ ์‹ค์ œ ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ์— ๊ธฐ์—ฌํ•˜์ง€ ์•Š๋Š” ๋ถ€๋ถ„์„ ๊นŠ์€ ์žฌ๊ท€ ์ „์— ์ฐพ์•„์„œ ์ œ๊ฑฐํ•˜๋Š” ์—ฌ๋Ÿฌ ์กฐ๊ฑด๋“ค์„ ๋‚˜์—ดํ•œ๋‹ค. ํ•œํŽธ, ์ž๊ฐ€ ๊ต์ฐจ๊ฐ€ ์ œ๊ฑฐ๋œ ์˜คํ”„์…‹ ๊ณก์„  ๋ฐ ๊ณก๋ฉด์€ ๊ธฐ์ € ๊ณก์„  ๋ฐ ๊ณก๋ฉด์˜ ๋ณด๋กœ๋…ธ์ด ๊ตฌ์กฐ์™€ ๊นŠ์€ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์ด ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž์œ  ๊ณก๋ฉด์˜ ์—ฐ์†๋œ ์˜คํ”„์…‹ ๊ณก๋ฉด๋“ค๋กœ๋ถ€ํ„ฐ ์ž์œ  ๊ณก๋ฉด์˜ ๋ณด๋กœ๋…ธ์ด ๊ตฌ์กฐ๋ฅผ ์œ ์ถ”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ํŠนํžˆ, ์˜คํ”„์…‹ ๊ณก๋ฉด์˜ ์ž๊ฐ€ ๊ต์ฐจ ๊ณก์„  ์ƒ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ€์ง€ ์ ์ด๋‚˜ ๋ง๋‹จ ์ ๊ณผ ๊ฐ™์€ ํŠน์ด์ ๋“ค์ด ์ž์œ  ๊ณก๋ฉด์˜ ๋ณด๋กœ๋…ธ์ด ๊ตฌ์กฐ์—์„œ ์–ด๋–ป๊ฒŒ ํ•ด์„๋˜๋Š”์ง€ ์ œ์‹œํ•œ๋‹ค.1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives and Approach 7 1.3 Contributions and Thesis Organization 11 2. Preliminaries 14 2.1 Curve and Surface Representation 14 2.1.1 Bezier Representation 14 2.1.2 B-spline Representation 17 2.2 Differential Geometry of Curves and Surfaces 19 2.2.1 Differential Geometry of Curves 19 2.2.2 Differential Geometry of Surfaces 21 3. Previous Work 23 3.1 Offset Curves 24 3.2 Offset Surfaces 27 3.3 Offset Curves on Surfaces 29 4. Trimming Offset Curve Self-intersections 32 4.1 Experimental Results 35 5. Trimming Offset Surface Self-intersections 38 5.1 Constraint Equations for Offset Self-Intersections 38 5.1.1 Coplanarity Constraint 39 5.1.2 Equi-angle Constraint 40 5.2 Removing Trivial Solutions 40 5.3 Removing Normal Flips 41 5.4 Multivariate Solver for Constraints 43 5.A Derivation of f(u,v) 46 5.B Relationship between f(u,v) and Curvatures 47 5.3 Trimming Offset Surfaces 50 5.4 Experimental Results 53 5.5 Summary 57 6. Acceleration of trimming offset curves and surfaces 62 6.1 Motivation 62 6.2 Basic Approach 67 6.3 Trimming an Offset Curve using the BVH 70 6.4 Trimming an Offset Surface using the BVH 75 6.4.1 Offset Surface BVH 75 6.4.2 Finding Self-intersections in Offset Surface Using BVH 87 6.4.3 Tracing Self-intersection Curves 98 6.5 Experimental Results 100 6.6 Summary 106 7. Application of Trimming Offset Surfaces: 3D Voronoi Diagram 107 7.1 Background 107 7.2 Approach 110 7.3 Experimental Results 112 7.4 Summary 114 8. Conclusion 119 Bibliography iDocto

    Implementing intersection calculations of the ray tracing algorithm with systolic arrays

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    Ray tracing is one technique that has been used to synthesize realistic images with a computer. Unfortunately, this technique, when implemented in software, is slow and expensive. The trend in computer graphics has been toward the use of special purpose hardware, to speed up the calculations, and, hence, the generation of the synthesized image. This paper describes the design and the operation of a systolic based architecture, tailored to speed up the intersection calculations, that must be performed as a part of the ray tracing algorithm

    Cuckoo Search with Lรฉvy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting

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    ABSTRACT. The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task.The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.This research has been kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project Ref. no. TIN2012-30768, Toho University (Funabashi, Japan), and the University of Cantabria (Santander, Spain)

    Doctor of Philosophy

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    dissertationThe medial axis of an object is a shape descriptor that intuitively presents the morphology or structure of the object as well as intrinsic geometric properties of the objectรขโ‚ฌโ„ขs shape. These properties have made the medial axis a vital ingredient for shape analysis applications, and therefore the computation of which is a fundamental problem in computational geometry. This dissertation presents new methods for accurately computing the 2D medial axis of planar objects bounded by B-spline curves, and the 3D medial axis of objects bounded by B-spline surfaces. The proposed methods for the 3D case are the first techniques that automatically compute the complete medial axis along with its topological structure directly from smooth boundary representations. Our approach is based on the eikonal (grassfire) flow where the boundary is offset along the inward normal direction. As the boundary deforms, different regions start intersecting with each other to create the medial axis. In the generic situation, the (self-) intersection set is born at certain creation-type transition points, then grows and undergoes intermediate transitions at special isolated points, and finally ends at annihilation-type transition points. The intersection set evolves smoothly in between transition points. Our approach first computes and classifies all types of transition points. The medial axis is then computed as a time trace of the evolving intersection set of the boundary using theoretically derived evolution vector fields. This dynamic approach enables accurate tracking of elements of the medial axis as they evolve and thus also enables computation of topological structure of the solution. Accurate computation of geometry and topology of 3D medial axes enables a new graph-theoretic method for shape analysis of objects represented with B-spline surfaces. Structural components are computed via the cycle basis of the graph representing the 1-complex of a 3D medial axis. This enables medial axis based surface segmentation, and structure based surface region selection and modification. We also present a new approach for structural analysis of 3D objects based on scalar functions defined on their surfaces. This approach is enabled by accurate computation of geometry and structure of 2D medial axes of level sets of the scalar functions. Edge curves of the 3D medial axis correspond to a subset of ridges on the bounding surfaces. Ridges are extremal curves of principal curvatures on a surface indicating salient intrinsic features of its shape, and hence are of particular interest as tools for shape analysis. This dissertation presents a new algorithm for accurately extracting all ridges directly from B-spline surfaces. The proposed technique is also extended to accurately extract ridges from isosurfaces of volumetric data using smooth implicit B-spline representations. Accurate ridge curves enable new higher-order methods for surface analysis. We present a new definition of salient regions in order to capture geometrically significant surface regions in the neighborhood of ridges as well as to identify salient segments of ridges

    A Line/Trimmed NURBS Surface Intersection Algorithm Using Matrix Representations

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    International audienceWe contribute a reliable line/surface intersection method for trimmed NURBS surfaces, based on a novel matrix-based implicit representation and numerical methods in linear algebra such as singular value decomposition and the computation of generalized eigenvalues and eigenvectors. A careful treatment of degenerate cases makes our approach robust to intersection points with multiple pre-images. We then apply our intersection algorithm to mesh NURBS surfaces through Delaunay refinement. We demonstrate the added value of our approach in terms of accuracy and treatment of degenerate cases, by providing comparisons with other intersection approaches as well as a variety of meshing experiments
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