We present MATch , a method to automatically convert photographs of material samples into production-grade procedural material models. At the core of MATch is a new library DiffMat that provides differentiable building blocks for constructing procedural materials, and automatic translation of large-scale procedural models, with hundreds to thousands of node parameters, into differentiable node graphs. Combining these translated node graphs with a rendering layer yields an end-to-end differentiable pipeline that maps node graph parameters to rendered images. This facilitates the use of gradient-based optimization to estimate the parameters such that the resulting material, when rendered, matches the target image appearance, as quantified by a style transfer loss. In addition, we propose a deep neural feature-based graph selection and parameter initialization method that efficiently scales to a large number of procedural graphs. We evaluate our method on both rendered synthetic materials and real materials captured as flash photographs. We demonstrate that MATch can reconstruct more accurate, general, and complex procedural materials compared to the state-of-the-art. Moreover, by producing a procedural output, we unlock capabilities such as constructing arbitrary-resolution material maps and parametrically editing the material appearance.

中文翻译：

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匹配

我们提出匹配，一种将材料样本的照片自动转换为生产级程序材料模型的方法。MATch 的核心是一个新的库差异矩阵它为构建程序材料提供了可微的构建块，并将具有数百到数千个节点参数的大规模程序模型自动转换为可微节点图。将这些翻译后的节点图与渲染层相结合会产生一个端到端的可微管道，它将节点图参数映射到渲染图像。这有助于使用基于梯度的优化来估计参数，以便在渲染时生成的材料与目标图像外观相匹配，如通过样式转移损失量化的那样。此外，我们提出了一种基于深度神经特征的图选择和参数初始化方法，可以有效地扩展到大量程序图。我们在渲染合成材料和捕捉为闪光照片的真实材料上评估我们的方法。我们证明，与最先进的技术相比，MATch 可以重建更准确、更通用和更复杂的程序材料。此外，通过生成程序输出，我们解锁了构建任意分辨率材质贴图和参数化编辑材质外观等功能。