樹狀結構陰影分解

摘要

我們研究如何從單張圖像中推斷出樹狀結構的物體陰影表示。先前的研究通常使用參數化或測量表示來建模陰影，但這兩者都不易解釋也不容易編輯。我們提出使用陰影樹表示法，結合基本陰影節點和合成方法，以分解物體表面的陰影。這種陰影樹表示法使得不熟悉物理陰影過程的新手用戶可以以高效且直觀的方式編輯物體陰影。推斷陰影樹的主要挑戰在於推斷問題涉及離散樹結構和樹節點的連續參數。我們提出了一種混合方法來應對這個問題。我們引入自回歸推斷模型來生成對樹結構和節點參數的粗略估計，然後通過優化算法對推斷出的陰影樹進行微調。我們在合成圖像、捕獲的反射、真實圖像和非現實主義的向量圖中進行實驗，從而支持後續應用，如材料編輯、向量化陰影和重燈。項目網站：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

樹狀結構陰影分解

Tree-Structured Shading Decomposition

摘要

Support