Intrinsic image decomposition is fundamental for visual understanding, as RGB images entangle material properties, illumination, and view-dependent effects. Recent diffusion-based methods have achieved strong results for single-view intrinsic decomposition; however, extending these approaches to multi-view settings remains challenging, often leading to severe view inconsistency. We propose \textbf{Intrinsic Decomposition Transformer (IDT)}, a feed-forward framework for multi-view intrinsic image decomposition. By leveraging transformer-based attention to jointly reason over multiple input images, IDT produces view-consistent intrinsic factors in a single forward pass, without iterative generative sampling. IDT adopts a physically grounded image formation model that explicitly decomposes images into diffuse reflectance, diffuse shading, and specular shading. This structured factorization separates Lambertian and non-Lambertian light transport, enabling interpretable and controllable decomposition of material and illumination effects across views. Experiments on both synthetic and real-world datasets demonstrate that IDT achieves cleaner diffuse reflectance, more coherent diffuse shading, and better-isolated specular components, while substantially improving multi-view consistency compared to prior intrinsic decomposition methods.

翻译：本征图像分解是视觉理解的基础，因为RGB图像将材质属性、光照和视角相关效应耦合在一起。基于扩散的方法在单视角本征分解中已取得显著成果；然而，将这些方法扩展到多视角场景仍具挑战性，常导致严重的视角不一致性。我们提出**本征分解Transformer（IDT）**，一种用于多视角本征图像分解的前馈框架。通过利用基于Transformer的注意力机制对多个输入图像进行联合推理，IDT可在单次前向传播中生成视角一致的本征因子，无需迭代生成采样。IDT采用基于物理的图像形成模型，将图像显式分解为漫反射率、漫反射着色和高光着色。这种结构化分解分离了朗伯与非朗伯光传输，实现了跨视角材质与光照效应的可解释、可控分解。在合成与真实数据集上的实验表明，相较于现有本征分解方法，IDT能获得更清晰的漫反射率、更连贯的漫反射着色、更独立的高光分量，并显著提升了多视角一致性。