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Unbiased, adaptive stochastic sampling for rendering inhomogeneous participating media

机译:无偏、自适应随机采样,用于渲染非均匀参与媒体

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摘要

Realistic rendering of participating media is one of the major subjects in computer graphics. Monte Carlo techniques are widely used for realistic rendering because they provide unbiased solutions, which converge to exact solutions. Methods based on Monte Carlo techniques generate a number of light paths, each of which consists of a set of randomly selected scattering events. Finding a new scattering event requires free path sampling to determine the distance from the previous scattering event, and is usually a time-consuming process for inhomogeneous participating media. To address this problem, we propose an adaptive and unbiased sampling technique using kd-tree based space partitioning. A key contribution of our method is an automatic scheme that partitions the spatial domain into sub-spaces (partitions) based on a cost model that evaluates the expected sampling cost. The magnitude of performance gain obtained by our method becomes larger for more inhomogeneous media, and rises to two orders compared to traditional free path sampling techniques.
机译:参与媒体的真实渲染是计算机图形学的主要主题之一。蒙特卡罗技术广泛用于逼真的渲染,因为它们提供了无偏的解决方案,这些解决方案收敛到精确的解决方案。基于蒙特卡罗技术的方法产生许多光路,每个光路由一组随机选择的散射事件组成。寻找新的散射事件需要自由路径采样来确定与前一个散射事件的距离,对于不均匀的参与介质来说,这通常是一个耗时的过程。为了解决这个问题,我们提出了一种使用基于kd树的空间划分的自适应和无偏采样技术。我们方法的一个关键贡献是自动方案,该方案基于评估预期采样成本的成本模型将空间域划分为子空间(分区)。对于更不均匀的介质,我们的方法获得的性能增益幅度更大,与传统的自由路径采样技术相比,性能增益上升到两个数量级。

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