We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernel-predicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and corresponding feature maps, which are consecutively reconstructed by a denoising module. The components are predicted based on statistics aggregated at the pixel level by the renderer. Denoising these components individually allows the use of per-component kernels that adapt to each component's noisy signal characteristics. Experimentally, we show that the proposed decomposition module consistently improves the denoising quality of current state-of-the-art kernel-predicting denoisers on large-scale academic and production datasets.

中文翻译：

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高质量蒙特卡罗渲染的深度合成降噪

我们提出了一种深度学习方法，用于将嘈杂的蒙特卡罗渲染自动分解为内核预测降噪器可以更有效地降噪的组件。在我们的模型中，神经分解模块学习预测噪声成分和相应的特征图，这些特征图由去噪模块连续重建。组件是根据渲染器在像素级别聚合的统计数据来预测的。单独对这些组件去噪允许使用适应每个组件的噪声信号特征的每个组件内核。通过实验，我们表明所提出的分解模块在大规模学术和生产数据集上持续提高了当前最先进的内核预测降噪器的降噪质量。