Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel, resulting in long rendering time. To reduce the cost, one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel (as little as 128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning: they use convolutional neural networks to learn the relationship between noisy images and reference images, using auxiliary features such as position and normal together with image color as inputs. The network predicts kernels which are then applied to the noisy input. These methods show powerful denoising ability, but tend to lose geometric or lighting details and to blur sharp features during denoising.In this paper, we solve this issue by proposing a novel network structure, a new input feature—light transport covariance from path space—and an improved loss function. Our network separates feature buffers from the color buffer to enhance detail effects. The features are extracted separately and then integrated into a shallow kernel predictor. Our loss function considers perceptual loss, which also improves detail preservation. In addition, we use a light transport covariance feature in path space as one of the features, which helps to preserve illumination details. Our method denoises Monte Carlo path traced images while preserving details much better than previous methods.

中文翻译：

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蒙特卡洛去噪的细节保留神经网络模型

电影制作中广泛使用基于蒙特卡洛的方法，例如路径跟踪。为了实现低噪声，它们每个像素需要很多样本，从而导致较长的渲染时间。为了降低成本，一种解决方案是蒙特卡洛降噪，即使用每个像素更少的样本（少至128个）渲染图像，然后对所得图像进行降噪。许多蒙特卡洛去噪方法都依赖于深度学习：它们使用卷积神经网络来学习嘈杂图像和参考图像之间的关系，并使用位置和法线等辅助特征以及图像颜色作为输入。网络预测内核，然后将其应用于噪声输入。这些方法显示了强大的去噪能力，但是在去噪过程中往往会丢失几何或光照细节并模糊锐利的特征。我们通过提出一种新颖的网络结构，一种新的输入功能（来自路径空间的光传输协方差）和一种改进的损耗函数来解决此问题。我们的网络将功能缓冲区与颜色缓冲区分开，以增强细节效果。分别提取特征，然后将其集成到浅核预测器中。我们的损失函数考虑了感知损失，这也改善了细节保存。另外，我们在路径空间中使用光传输协方差特征作为特征之一，这有助于保留照明细节。与以前的方法相比，我们的方法对蒙特卡洛路径跟踪图像进行了降噪处理，同时保留了细节。我们的网络将功能缓冲区与颜色缓冲区分开，以增强细节效果。分别提取特征，然后将其集成到浅核预测器中。我们的损失函数考虑了感知损失，这也改善了细节保存。另外，我们在路径空间中使用光传输协方差特征作为特征之一，这有助于保留照明细节。与以前的方法相比，我们的方法对蒙特卡洛路径跟踪图像进行了降噪处理，同时保留了细节。我们的网络将功能缓冲区与颜色缓冲区分开，以增强细节效果。分别提取特征，然后将其集成到浅核预测器中。我们的损失函数考虑了感知损失，这也改善了细节保存。另外，我们在路径空间中使用光传输协方差特征作为特征之一，这有助于保留照明细节。与以前的方法相比，我们的方法对蒙特卡洛路径跟踪图像进行了降噪处理，同时保留了细节。