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

从单个图像中推断对象着色的树结构表示，通过使用遮蔽树表示结合基本遮蔽节点和合成方法，分解对象表面着色，使不熟悉物理着色过程的初学者能以高效直观的方式编辑对象着色，该方法通过混合方法解决了离散树结构和树节点的连续参数的推断问题，并通过优化算法对推断的遮蔽树进行微调，研究结果适用于材料编辑，矢量化着色和重照等下游应用。

树结构阴影分解