基于多分辨率采样的快速Monte Carlo绘制
Fast Monte Carlo Rendering via Multi-Resolution Sampling
摘要：
蒙特卡罗绘制算法被广泛用于产生真实感计算机图形图像。然而，这些算法需要对每个像素的大量光线进行采样，以实现适当的全局光照，因此需要大量的计算。在本文中，我们提出了一种混合绘制方法，以加快蒙特卡罗绘制算法。我们的方法首先生成两个版本的渲染：一个是低分辨率和高采样率（LRHS），另一个是高分辨率和低采样率（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个高质量的图像，就好像它是以高分辨率和高采样率渲染的一样。具体来说，我们制定这个融合任务作为一个超分辨率的问题，从低分辨率输入（LRHS），辅助HRLS渲染生成一个高分辨率渲染。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，我们的混合渲染算法比最先进的蒙特卡洛去噪方法要快得多，同时在我们自己的BCR数据集和Gharbi数据集上进行测试时，渲染出高质量的图像。\url{https://github.com/hqqxyy/msspl}
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
