Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes

• URL: http://arxiv.org/abs/2503.09993v1
• Date: Thu, 13 Mar 2025 03:04:35 GMT
• Title: Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes
• Authors: JunYong Choi, Min-Cheol Sagong, SeokYeong Lee, Seung-Won Jung, Ig-Jae Kim, Junghyun Cho,
• Abstract summary: Inverse rendering is inherently ill-posed, making it difficult to predict a single accurate solution.<n>We propose a diffusion-based inverse rendering framework that decomposes a single RGB image into geometry, material, and lighting.
• Score: 18.42978937263707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a diffusion-based inverse rendering framework that decomposes a single RGB image into geometry, material, and lighting. Inverse rendering is inherently ill-posed, making it difficult to predict a single accurate solution. To address this challenge, recent generative model-based methods aim to present a range of possible solutions. However, finding a single accurate solution and generating diverse solutions can be conflicting. In this paper, we propose a channel-wise noise scheduling approach that allows a single diffusion model architecture to achieve two conflicting objectives. The resulting two diffusion models, trained with different channel-wise noise schedules, can predict a single highly accurate solution and present multiple possible solutions. The experimental results demonstrate the superiority of our two models in terms of both diversity and accuracy, which translates to enhanced performance in downstream applications such as object insertion and material editing.

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