曲面非球面参数
Jonathan Dupuy, Eric Heitz and Laurent Belcour
乔纳森·杜普(Jonathan Dupuy)，埃里克·海茨(Eric Heitz)和洛朗·贝尔库(Laurent Belcour)
ACM SIGGRAPH 2017
ACM SIGGRAPH 2017
抽象 (Abstract)
We introduce a novel parameterization for spherical distributions that is based on a point located inside the sphere, which we call a pivot. The pivot serves as the center of a straight-line projection that maps solid angles onto the opposite side of the sphere. By transforming spherical distributions in this way, we derive novel parametric spherical distributions that can be evaluated and importance-sampled from the original distributions using simple, closed-form expressions. Moreover, we prove that if the original distribution can be sampled and/or integrated over a spherical cap, then so can the transformed distribution. We exploit the properties of our parameterization to derive efficient spherical lighting techniques for both real-time and offline rendering. Our techniques are robust, fast, easy to implement, and achieve quality that is superior to previous work.
我们介绍了一种新颖的球形分布参数化方法，该方法基于位于球体内的一个点(称为枢轴)。 枢轴充当直线投影的中心，该直线投影将立体角映射到球体的另一侧。 通过以这种方式变换球面分布，我们可以得出新颖的参数球面分布，可以使用简单的闭式表达式从原始分布中进行评估和重要性采样。 此外，我们证明，如果原始分布可以在球形帽上采样和/或积分，则变换后的分布也可以。 我们利用参数化的属性来导出用于实时和脱机渲染的有效球形照明技术。 我们的技术强大，快速，易于实施，并且质量优于以前的工作。
资料下载 (Downloads)
曲面非球面参数