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Applications of Surface Networks to Sampling Problems in Computer Graphics

机译:曲面网络在计算机图形学中抽样问题中的应用

摘要

This thesis develops the theory, algorithms and data structures for adaptive sampling of parametric functions, which can represent the shapes and motions of physical objects. For the first time, ensured methods are derived for determining collisions and other interactions for a broad class of parametric functions. A new data structure, called a surface network, is developed for the collision algorithm and for other sampling problems in computer graphics. A surface network organizes a set of parametric samples into a hierarchy. Surface networks are shown to be good for rendering images, for approximating surfaces, and for modeling physical environments. The basic notion of a surface network is generalized to higher-dimensional problems such as collision detection. We may think of a two-dimensional network covering a three-dimensional solid, or an n-dimensional network embedded in a higher-dimensional space. Surface networks are applied to the problems of adaptive sampling of static parametric surfaces, to adaptive sampling of time-dependent parametric surfaces, and to a variety of applications in computer graphics, robotics, and aviation. First we develop the theory for adaptive sampling of static surfaces. We explore bounding volumes that enclose static surfaces, subdivision mechanisms that adjust the sampling density, and subdivision criteria that determine where samples should be placed. A new method is developed for creating bounding ellipsoids of parametric surfaces using a Lipschitz condition to place bounds on the derivatives of parametric functions. The bounding volumes are arranged in a hierarchy based on the hierarchy of the surface network. The method ensures that the bounding volume hierarchy contains the parametric surface completely. The bounding volumes are useful for computing surface intersections. They are potentially useful for ray tracing of parametric surfaces. We develop and examine a variety of subdivision mechanisms to control the sampling process for parametric functions. Some of the methods are shown to improve the robustness of adaptive sampling. Algorithms for one mechanism, using bintrees of right parametric triangles, are particularly simple and robust. A set of empirical subdivision criteria determine where to sample a surface, when we have no additional information about the surface. Parametric samples are concentrated in regions of high curvature, and along intersection boundaries. Once the foundations of adaptive sampling for static surfaces are described, we examine time-dependent surfaces. Based on results with the empirical subdivision criteria for static surfaces, we derive ensured criteria for collision determination. We develop a new set of rectangular bounding volumes, apply a standard L-dimensional subdivision mechanism called k-d trees, and develop criteria for ensuring that we detect collisions between parametric surfaces. We produce rectangular bounding boxes using a "Jacobian''-style matrix of Lipschitz conditions on the parametric function. The rectangular method produces even tighter bounds on the surface than the ellipsoidal method, and is effective for computing collisions between parametric surfaces. A new collision determination technique is developed that can detect collisions of parametric functions, based on surface network hierarchies. The technique guarantees that the first collision is found, to within the temporal accuracy of the computation, for surfaces with bounded parametric derivatives. Alternatively, it is possible to guarantee that no collisions occur for the same class of surfaces. When a collision is found, the technique reports the location and parameters of the collision as well as the time of first collision. Finally, we examine several applications of the sampling methods. Surface networks are applied to the problem of converting a two-dimensional image, or texture map, into a set of triangles that tile the plane. Many polygon-rendering systems do not provide the capability of rendering surfaces with textures. The technique converts textures to triangles that can be rendered directly by a polygon system. In addition, potential applications of the collision determination techniques are discussed, including robotics and air-traffic control problems.
机译:本文提出了参数函数的自适应采样的理论,算法和数据结构,可以表示物理对象的形状和运动。首次,为各种参数函数确定了确定碰撞和其他相互作用的可靠方法。针对碰撞算法和计算机图形学中的其他采样问题,开发了一种称为表面网络的新数据结构。地面网络将一组参数样本组织到一个层次结构中。曲面网络被证明对渲染图像,近似曲面和对物理环境建模非常有用。表面网络的基本概念被推广到更高维度的问题,例如碰撞检测。我们可能会想到覆盖三维实体的二维网络,或者嵌入更高维空间的n维网络。曲面网络被应用于静态参数化曲面的自适应采样问题,与时间相关的参数化曲面的自适应采样问题,以及应用于计算机图形学,机器人技术和航空领域的各种应用。首先,我们开发用于静态表面自适应采样的理论。我们探索包围静态表面的边界体积,调整采样密度的细分机制以及确定应将样品放置在何处的细分标准。开发了一种新的方法,该方法使用Lipschitz条件将边界放置在参数函数的导数上,从而创建参数表面的有界椭球。边界体积基于表面网络的层次结构按层次结构排列。该方法确保边界体积层次完全包含参数曲面。边界体积对于计算曲面相交很有用。它们对于参数化曲面的光线跟踪很有用。我们开发并研究了各种细分机制,以控制参数函数的采样过程。某些方法显示可以提高自适应采样的鲁棒性。使用右参数三角形的二叉树的一种机制的算法特别简单且健壮。当我们没有关于该表面的其他信息时,一组经验细分标准将确定在何处对表面进行采样。参数样本集中在高曲率区域以及沿相交边界。一旦描述了静态曲面的自适应采样的基础,我们便会检查与时间有关的曲面。基于对静态曲面的经验细分标准的结果,我们得出确定碰撞的确定标准。我们开发了一组新的矩形边界体积,应用了称为k-d树的标准L维细分机制,并开发了用于确保检测参数面之间的碰撞的标准。我们使用参数函数上的Lipschitz条件的“ Jacobian”式矩阵生成矩形边界框,矩形方法在曲面上产生的边界比椭圆法更紧密,并且对于计算参数曲面之间的碰撞非常有效。开发了一种能够基于曲面网络层次结构检测参数函数碰撞的确定技术,该技术可确保在计算的时间精度内找到具有有界参数导数的曲面的第一次碰撞。保证在同一类表面上不会发生碰撞,当发现碰撞时,该技术会报告碰撞的位置和参数以及第一次碰撞的时间,最后,我们研究了采样方法的几种应用。适用于将二维图像或纹理贴图转换为一组平铺平面的三角形。许多多边形渲染系统不提供使用纹理渲染表面的功能。该技术将纹理转换为可以由多边形系统直接渲染的三角形。此外,还讨论了碰撞确定技术的潜在应用,包括机器人技术和空中交通管制问题。

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    Von Herzen Brian;

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  • 年度 1988
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