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Interactive Visualization of Rotational Symmetry Fields on Surfaces

机译:曲面上旋转对称场的交互式可视化

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Rotational symmetries (RoSys) have found uses in several computer graphics applications, such as global surface parameterization, geometry remeshing, texture and geometry synthesis, and nonphotorealistic visualization of surfaces. The visualization of N-way rotational symmetry (N-RoSy) fields is a challenging problem due to the ambiguities in the N directions represented by an N-way symmetry. We provide an algorithm that allows faithful and interactive representation of N-RoSy fields in the plane and on surfaces, by adapting the well-known line integral convolution (LIC) technique from vector and second-order tensor fields. Our algorithm captures N directions associated with each point in a given field by decomposing the field into multiple different vector fields, generating LIC images of these fields, and then blending the results. To address the loss of contrast caused by the blending of images, we observe that the pixel values in LIC images closely approximate normally distributed random variables. This allows us to use concepts from probability theory to correct the loss of contrast without the need to perform any image analysis at each frame.
机译:旋转对称(RoSys)已在多种计算机图形应用程序中使用,例如全局表面参数化,几何重新网格化,纹理和几何合成以及表面的非真实感可视化。由于N方向对称性表示的N方向上的歧义性,N方向旋转对称(N-RoSy)场的可视化是一个具有挑战性的问题。我们提供了一种算法,该算法通过从矢量和二阶张量场改编众所周知的线积分卷积(LIC)技术,可以在平面和表面上忠实且交互式地表示N-RoSy场。我们的算法通过将一个场分解成多个不同的向量场,生成这些场的LIC图像,然后将结果混合,来捕获与给定场中每个点相关的N个方向。为了解决由图像融合导致的对比度损失,我们注意到LIC图像中的像素值非常接近正态分布的随机变量。这使我们可以使用概率论中的概念来校正对比度的损失,而无需在每个帧上执行任何图像分析。

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