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Irradiance regression for efficient final gathering in global illumination

机译:辐照度回归可在全局照明下进行有效的最终聚集

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摘要

Photon mapping is widely used for global illumination rendering because of its high computational efficiency. But its efficiency is still limited, mainly by the intensive sampling required in final gathering, a process that is critical for removing low frequency artifacts of density estimation. In this paper, we propose a method to predict the final gathering estimation with direct density estimation, thereby achieving high quality global illumination by photon mapping with high efficiency. We first sample the irradiance of a subset of shading points by both final gathering and direct radiance estimation. Then we use the samples as a training set to predict the final gathered irradiance of other shading points through regression. Consequently, we are able to achieve about three times overall speedup compared with straightforward final gathering in global illumination computation with the same rendering quality.
机译:光子映射因其高计算效率而被广泛用于全局照明渲染。但是它的效率仍然受到限制,主要是由于在最终采集中需要进行大量采样,这一过程对于消除密度估计的低频伪像至关重要。在本文中,我们提出了一种通过直接密度估计来预测最终聚集估计的方法,从而通过光子映射高效地实现了高质量的全局照明。我们首先通过最终采集和直接辐射估计来采样阴影点子集的辐照度。然后,我们将这些样本用作训练集,以通过回归预测其他阴影点的最终聚集辐照度。因此,与具有相同渲染质量的全局照明计算中的直接最终收集相比,我们能够实现大约三倍的总体加速。

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