树形结构的着色分解

摘要

我们研究从单个图像推断树状结构表示以用于物体着色。先前的研究通常使用参数化或测量表示来建模着色，这既不易解释也不易编辑。我们提出使用阴影树表示，结合基本着色节点和合成方法来分解物体表面着色。阴影树表示使得不熟悉物理着色过程的初学者能够以高效且直观的方式编辑物体着色。推断阴影树的一个主要挑战在于推断问题涉及离散树结构和树节点的连续参数。我们提出了一种混合方法来解决这个问题。我们引入自回归推断模型来生成树结构和节点参数的粗略估计，然后通过优化算法对推断的阴影树进行微调。我们展示了对合成图像、捕获的反射、真实图像以及非现实矢量图的实验，从而支持后续应用，如材质编辑、矢量化着色和重照。项目网站：https://chen-geng.com/inv-shade-trees

English

We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-trees

树形结构的着色分解

Tree-Structured Shading Decomposition

摘要

Support