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Intrinsic Image Diffusion for Indoor Single-view Material Estimation
March 22, 2024, 4:46 a.m. | Peter Kocsis (Technical University of Munich), Vincent Sitzmann (MIT EECS), Matthias Nie{\ss}ner (Technical University of Munich)
cs.CV updates on arXiv.org arxiv.org
Abstract: We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue, we advocate for a probabilistic formulation, where instead of attempting to directly predict the …
abstract arxiv challenge computer computer vision cs.ai cs.cv cs.gr diffusion generative image image diffusion intrinsic maps material multiple sample type view vision
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