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Stochastic progressive photon mapping

机译:随机逐行光子映射

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This paper presents a simple extension of progressive photon mapping for simulating global illumination with effects such as depth-of-field, motion blur, and glossy reflections. Progressive photon mapping is a robust global illumination algorithm that can handle complex illumination settings including specular-diffuse-specular paths. The algorithm can compute the correct radiance value at a point in the limit. However, progressive photon mapping is not effective at rendering distributed ray tracing effects, such as depth-of-field, that requires multiple pixel samples in order to compute the correct average radiance value over a region. In this paper, we introduce a new formulation of progressive photon mapping, called stochastic progressive photon mapping, which makes it possible to compute the correct average radiance value for a region. The key idea is to use shared photon statistics within the region rather than isolated photon statistics at a point. The algorithm is easy to implement, and our results demonstrate how it efficiently handles scenes with distributed ray tracing effects, while maintaining the robustness of progressive photon mapping in scenes with complex lighting.
机译:本文介绍了逐步光子映射的简单延伸,用于模拟具有诸如景深,运动模糊和光泽反射的效果的全局照明。渐进光子映射是一种坚固的全局照明算法,可以处理包括镜面漫射镜面路径的复杂照明设置。该算法可以在极限的点处计算正确的辐射值。然而,渐进光子映射在渲染分布式光线跟踪效果(例如场景)时无效,这需要多个像素样本,以便在区域上计算正确的平均光线值。在本文中,我们介绍了一种新的渐进光子映射的制剂,称为随机逐行光子映射,这使得可以计算区域的正确平均辐射值。关键的想法是在区域内使用该区域内的共享光子统计信息而不是在一个点处的隔离光子统计。该算法易于实现,我们的结果表明它如何有效处理具有分布式光线跟踪效果的场景,同时保持逐行光子映射在具有复杂照明的场景中的鲁棒性。

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