Monte Carlo rendering algorithms are widely used to produce photorealistic computer graphics images. However, these algorithms need to sample a substantial amount of rays per pixel to enable proper global illumination and thus require an immense amount of computation. In this paper, we present a hybrid rendering method to speed up Monte Carlo rendering algorithms. Our method first generates two versions of a rendering: one at a low resolution with a high sample rate (LRHS) and the other at a high resolution with a low sample rate (HRLS). We then develop a deep convolutional neural network to fuse these two renderings into a high-quality image as if it were rendered at a high resolution with a high sample rate. Specifically, we formulate this fusion task as a super resolution problem that generates a high resolution rendering from a low resolution input (LRHS), assisted with the HRLS rendering. The HRLS rendering provides critical high frequency details which are difficult to recover from the LRHS for any super resolution methods. Our experiments show that our hybrid rendering algorithm is significantly faster than the state-of-the-art Monte Carlo denoising methods while rendering high-quality images when tested on both our own BCR dataset and the Gharbi dataset. \url{https://github.com/hqqxyy/msspl}

中文翻译：

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通过多分辨率采样的快速蒙特卡罗渲染

蒙特卡罗渲染算法被广泛用于生成逼真的计算机图形图像。然而，这些算法需要对每个像素的大量光线进行采样以实现适当的全局照明，因此需要大量的计算。在本文中，我们提出了一种混合渲染方法来加速蒙特卡洛渲染算法。我们的方法首先生成两种版本的渲染：一种是低分辨率高采样率 (LRHS)，另一种是高分辨率低采样率 (HRLS)。然后，我们开发了一个深度卷积神经网络，将这两个渲染融合成一张高质量的图像，就好像它以高分辨率和高采样率渲染一样。具体来说，我们将此融合任务制定为超分辨率问题，该问题从低分辨率输入 (LRHS) 生成高分辨率渲染，并辅以 HRLS 渲染。HRLS 渲染提供了对于任何超分辨率方法都难以从 LRHS 恢复的关键高频细节。我们的实验表明，在我们自己的 BCR 数据集和 Gharbi 数据集上进行测试时，我们的混合渲染算法明显快于最先进的蒙特卡罗去噪方法，同时渲染高质量图像。\url{https://github.com/hqqxyy/msspl} 我们的实验表明，在我们自己的 BCR 数据集和 Gharbi 数据集上进行测试时，我们的混合渲染算法明显快于最先进的蒙特卡罗去噪方法，同时渲染高质量图像。\url{https://github.com/hqqxyy/msspl} 我们的实验表明，在我们自己的 BCR 数据集和 Gharbi 数据集上进行测试时，我们的混合渲染算法明显快于最先进的蒙特卡罗去噪方法，同时渲染高质量图像。\url{https://github.com/hqqxyy/msspl}