IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and
  Illuminations
Authors: name: Zhibing Li, name: Tong Wu, name: Jing Tan, name: Mengchen Zhang, name: Jiaqi Wang, name: Dahua Lin

Capturing geometric and material information from images remains a
fundamental challenge in computer vision and graphics. Traditional
optimization-based methods often require hours of computational time to
reconstruct geometry, material properties, and environmental lighting from
dense multi-view inputs, while still struggling with inherent ambiguities
between lighting and material. On the other hand, learning-based approaches
leverage rich material priors from existing 3D object datasets but face
challenges with maintaining multi-view consistency. In this paper, we introduce
IDArb, a diffusion-based model designed to perform intrinsic decomposition on
an arbitrary number of images under varying illuminations. Our method achieves
accurate and multi-view consistent estimation on surface normals and material
properties. This is made possible through a novel cross-view, cross-domain
attention module and an illumination-augmented, view-adaptive training
strategy. Additionally, we introduce ARB-Objaverse, a new dataset that provides
large-scale multi-view intrinsic data and renderings under diverse lighting
conditions, supporting robust training. Extensive experiments demonstrate that
IDArb outperforms state-of-the-art methods both qualitatively and
quantitatively. Moreover, our approach facilitates a range of downstream tasks,
including single-image relighting, photometric stereo, and 3D reconstruction,
highlighting its broad applications in realistic 3D content creation.