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Visualizing Nonmanifold and Singular Implicit Surfaces with Point Clouds

机译:使用点云可视化非流形和奇异隐式曲面

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We use octree spatial subdivision to generate point clouds on complex nonmanifold implicit surfaces in order to visualize them. The new spatial subdivision scheme only uses point sampling and an interval exclusion test. The algorithm includes a test for pruning the resulting plotting nodes so that only points in the closest nodes to the surface are used in rendering. This algorithm results in improved image quality compared to the naive use of intervals or affine arithmetic when rendering implicit surfaces, particularly in regions of high curvature. We discuss and compare CPU and GPU versions of the algorithm. We can now render nonmanifold features such as rays, ray-like tubes, cusps, ridges, thin sections that are at arbitrary angles to the octree node edges, and singular points located within plot nodes, all without artifacts. Our previous algorithm could not render these without severe aliasing. The algorithm can render the self-intersection curves of implicit surfaces by exploiting the fact that surfaces are singular where they self-intersect. It can also render the intersection curves of two implicit surfaces. We present new image space and object space algorithms for rendering these intersection curves as contours on one of the surfaces. These algorithms are better at rendering high curvature contours than our previous algorithms. To demonstrate the robustness of the node pruning algorithm we render a number of complex implicit surfaces such as high order polynomial surfaces and Gaussian curvature surfaces. We also compare the algorithm with ray casting in terms of speed and image quality. For the surfaces presented here, the point clouds can be computed in seconds to minutes on a typical Intel based PC. Once this is done, the surfaces can be rendered at much higher frame rates to allow some degree of interactive visualization.
机译:我们使用八叉树空间细分在复杂的非流形隐式曲面上生成点云,以使其可视化。新的空间细分方案仅使用点采样和间隔排除测试。该算法包括用于修剪所得绘制节点的测试,以便仅在与曲面最接近的节点中的点用于渲染。与在渲染隐含表面时(尤其是在高曲率区域中)朴素使用间隔或仿射算法相比,该算法可提高图像质量。我们讨论并比较该算法的CPU和GPU版本。现在,我们可以渲染非流形特征,例如射线,类似射线的管,尖点,山脊,与八叉树节点边缘成任意角度的薄片以及位于图节点内的奇异点,所有这些都没有伪像。没有严格的混叠,我们以前的算法无法渲染这些图像。该算法可以利用曲面在其自相交处是奇异的事实来渲染隐含曲面的自相交曲线。它还可以绘制两个隐式曲面的相交曲线。我们提出了新的图像空间和对象空间算法,用于将这些相交曲线渲染为曲面之一上的轮廓。与我们以前的算法相比,这些算法在渲染高曲率轮廓时效果更好。为了证明节点修剪算法的鲁棒性,我们渲染了许多复杂的隐式曲面,例如高阶多项式曲面和高斯曲率曲面。我们还在速度和图像质量方面将算法与射线投射进行了比较。对于此处介绍的表面,在典型的基于Intel的PC上,点云可以在几秒钟到几分钟内计算出来。完成此操作后,可以以更高的帧速率渲染表面,以实现某种程度的交互式可视化。

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